Web Designer Express is a reputable Miami-based company that has been in business for 20 years. With a team of experienced web designers and developers, they offer a wide range of services, including web design, e-commerce development, web development, and more. Their portfolio showcases over 10,000 websites designed, with a focus on creating custom, unique solutions for each client. With a presence in Miami, Florida, they cater to businesses and individuals seeking to establish a strong online presence. As a company, Web Designer Express is dedicated to building long-lasting relationships with their clients, providing personalized service, and exceeding expectations.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Mozello, a SIA, is an innovative website builder that empowers individuals and businesses to create their own unique, modern websites and online stores. With Mozello, users can choose from a range of professionally designed templates and customize their website's layout, colors, and content to fit their brand's identity. The platform offers a user-friendly interface, making it easy for anyone to build and manage their own website without requiring extensive technical skills. Mozello's solutions cater to a diverse range of customers, from entrepreneurs and bloggers to activists and businesses of all sizes.

Mozello's website builder is built for speed and ease, allowing users to create a website within a day. The platform's features are designed to help users succeed, including responsive design, powerful marketing and SEO tools, and a worry-free domain registration and web hosting solution. With Mozello, users can focus on what matters most - growing their business and online presence. The platform's customer support team is always available to help users overcome any challenges they may face, ensuring they can achieve their goals with ease. By choosing Mozello, users can rest assured that their online presence is in capable and reliable hands.

According to Cognitive Market Research, the global Web Design market size is USD 56815.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 22726.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 17044.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 13067.50 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.50% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 2840.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.90% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 1136.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031.
The Illustrative Web Design held the highest Web Design market revenue share in 2024.

Market Dynamics of Web Design Market

Key Drivers for Web Design Market

Responsive Design is a Fundamental Component of Modern Website Development to Increase the Demand Globally

Driving the extensive adoption of mobile devices, responsive design become an indispensable element in modern web development. A responsive website adjusts its visual and textual elements in real-time to correspond with the screen size and orientation of the user's device. This ensures a seamless and continuous user experience across an extensive range of devices, encompassing desktop computers, smartphones, and tablets. The aforementioned adaptability not only enhances usability but also satisfies the growing expectations of customers who predominately utilize mobile devices for internet access.

User Experience (UX) Design Pertains to the Development of an Online Platform to Propel Market Growth

The establishment of a website that is not only functional but also pleasurable to use constitutes User Experience (UX) design. It involves developing a website that effectively satisfies the needs and behaviors of users, as well as understanding their functions and desires. This consists of interactive elements that guide visitors towards their intended goals, concise and clear text, and user-friendly navigation. UX design that is effective reduces user resistance and promotes smooth experiences, allowing for effortless content engagement, information retrieval, and transaction completion. As a result, this promotes increased user satisfaction and devotion. By placing emphasis on the visual aesthetics and sensory experience of the website, User Interface (UI) design serves to improve User Experience (UX). The procedure involves the intention of creating visually appealing layouts, determining appropriate color schemes, and ensuring consistency throughout multiple pages.

Restraint Factor for the Web Design Market

Web Developers Encounter Significant Constraints as a Result of User Privacy Mandates

Web developers encounter significant constraints as a result of user privacy mandates. Data privacy legislation, exemplified by the General Data Protection Regulation (GDPR) in Europe, has implemented stringent controls regarding the collection and administration of user data. Compliance with these regulations adds complexity to web development processes, necessitating the integration of explicit consent mechanisms, data minimization strategies, robust data storage and security measures, and user rights-enabling mechanisms (e.g., access to and deletion of personal data). In order to adhere to these legislative obligations, online developers must conduct a thorough assessment of their data management procedures. This underscores the criticality of privacy compliance in the context of modern online development.

Impact of Covid-19 on the Web Design Market

The COVID-19 pandemic has significantly increased the demand for web development services due to an extensive range of factors. As a result of individuals choosing to remain indoors and abstain from attending in-person events, organizations were compelled to swiftly adapt by transferring their operations to the digital domain. Due to th...

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes - Special populations: No

Universe

All persons present in Cameroon at the time of the census, including visitors from other countries.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE DESIGN: Systematic sample of every 10th dwelling with a random start, drawn by MPC

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE UNIVERSE: Systematic sample of every 10th dwelling with a random start, drawn by MPC

SAMPLE SIZE (person records): 736,514

Mode of data collection

Face-to-face [f2f]

Research instrument

Two forms: Dwelling units and collective households

Response rate

UNDERCOUNT: 7%

This study includes a restricted data file, version 3, for Wave 2 of the L.A.FANS data. To compare L.A.FANS restricted data, version 3 with other restricted data versions, see the table on the series page for the L.A.FANS data here. Data in this study are designed for use with the public use data files for L.A.FANS, Wave 2 (study 2). This file adds only a few variables to the L.A.FANS, Wave 2 public use files. Specifically, it adds the census tract and block number for the tract each respondent lives in and geographic coordinates data for a number of locations reported by the respondent (including home, grocery store, place of work, place of worship, schools, etc.). It also includes certain variables, thought to be sensitive, which are not available in the public use data. These variables are identified in the L.A.FANS Wave 2 Users Guide and Codebook. Finally, some distance variables and individual characteristics which are treated in the public use data to make it harder to identify individuals are provided in an untreated form in the Version 3 restricted data file. Please note that L.A. FANS restricted data may only be accessed within the ICPSR Virtual Data Enclave (VDE) and must be merged with the L.A. FANS public data prior to beginning any analysis. A Users' Guide which explains the design and how to use the samples are available for Wave 2 at the RAND website. Additional information on the project, survey design, sample, and variables are available from: Sastry, Narayan, Bonnie Ghosh-Dastidar, John Adams, and Anne R. Pebley (2006). The Design of a Multilevel Survey of Children, Families, and Communities: The Los Angeles Family and Neighborhood Survey, Social Science Research, Volume 35, Number 4, Pages 1000-1024 The Users' Guides (Wave 1 and Wave 2) RAND Documentation Reports page

Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

Relationship









Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

The study aims to identify factors influencing pricing, understand how pricing affects purchase intention and customer satisfaction, and provide valuable insights for skincare businesses to develop effective pricing strategies in Indonesia’s skincare market. The study used a purposive sampling approach, collecting data from 123 respondents who were internet users in Indonesia in January 2024. These respondents have engaged with online retailers to purchase skin care products. The results found that website design affects e-service quality, affecting customer satisfaction and trust. Customer satisfaction and trust have a significant impact on repurchase intentions. This study recommends that skincare businesses focus on website design, provide high-quality e-services, and implement customer satisfaction and loyalty programs. Future research can explore additional factors and conduct similar studies in different locations or product categories.

Keywords—Website design, skin care, e-marketplace, purchase intention, customer satisfaction, customer trust, e-service quality.

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes (institutional) - Special populations: Homeless, boat people

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building and usually live together and eat together from one kitchen. One kitchen means that the daily needs are managed and combined into one. - Group quarters: An institutional household includes people living in a dormitory, barracks, or insitution where everyday needs are managed by an institution or foundation. Also includes groups of 10 or more people in lodging houses or buildings.

Universe

All population, Indonesian and foreign, residing in the territorial area of Indonesia, regardless of residence status. Includes homeless, refugees, ship crews, and people in inaccessible areas. Diplomats and their families residing in Indonesia were excluded.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics Indonesia

SAMPLE DESIGN: Geographically stratified systematic sample (drawn by MPC).

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 22,928,795

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires: C1 to enumerate regular households living in areas covered in the census mappling; C2 for the population living in areas not included in the mapping, such as remote areas; and L2 for the homeless, boat people, and tribes.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: This is a building persons use solely for lodging purposes. Buildings which are not used for lodging such factories or warehouses as are not dwellings. - Households: The group of people related or not that usually live together in one or more dwellings, eat together, and together take care of their daily fundamental needs. Usually a household is made up of a man, woman, and their children. Other relatives, guests and servants are counted as part of the household if they spent the night preceding the census in the household. As an exception a household can be made up of a single person. - Group quarters: The group of people living together in camps, boarding schools, hospitals, prisons, and other collective households such as these.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Bureau of Statistics

SAMPLE DESIGN: Sample drawn by NBS from long form questionnaire. Weights provide expansion factors. Approximately 15% of rural enumeration areas within each district received the long form questionnaire; urban areas were sampled at a higher density. IPUMS drew a systematic two-thirds subsample to reduce the original dataset from 15 to 10%.

SAMPLE UNIT: Dwelling

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 3,732,735

Mode of data collection

Face-to-face [f2f]

Research instrument

A short questionnaire administered to 75% of enumeration areas and a long questionnaire adminstered to 25% of enumeration areas.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Point Conception map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Computer Assisted Telephone Interview

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Since 2019, the questionnaire for the GHS series changed and the variables were also renamed. For correspondence between old names (GHS pre-2019) and new name (GHS post-2019), see the document ghs-2019-variables-renamed.

Abstract

This survey reflects the data for the year 2009, and provides us with the main data about the inbound visitor and his/her behavior during the visit, such as the expenditures, main purpose of the visit, which is related to overnight-stay tourists. The survey provided data on: The characteristics of visitors The characteristics of the visit The length of stay of the visit The amount and mode of expenditure during the visit

Geographic coverage

West Bank Governerate

Analysis unit

Visitors to tourism areas

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population The target population for this study was all guests in hotels in the West Bank, whether resident or non resident, in 2009.

Sampling Frame Framework was created under the preview of visitor arrivals in 2008, and the frame is a list of arrivals by nationality, month, and the governorate. The preview window consists of the tourist sites in the north, central, and south of the West Bank, and Jerusalem, and regions where a police officer is at each site of the Ministry of Tourism and Antiquities and/or a tour guide accompanied the visitors.

Sample Size The sample size was estimated to be about 2500 visitors distributed among the West Bank sites and attractions.

Sample Survey Design This sample survey is a stratified random sample of visitors in the Palestinian territory and units are the areas in the West Bank (including regions: north, central, and south, and Jerusalem). A different sample of visitors at each site was selected depending on the number of visitors during the previous year 2008

Distribution Of The Sample The distributed sample of visitors to tourist sites frequented by visitors in a manner proportional to the number of visitors to sites during the previous year 2008

Mode of data collection

Face-to-face [f2f]

Response rate

NO RESPONSE

Sampling error estimates

-

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

For more see the statistical release.

Kind of data

Sample survey data

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses the Master Sample frame that has been developed as a general-purpose household survey frame. The 2013 Master Sample is based on information collected during the 2011 Census. There are 3 324 primary sampling units in the Master Sample, with an expected sample of approximately 33 000 dwelling units. The sampling procedure for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

The Master Sample is designed to be representative at the provincial level and within provinces at metro/non-metro levels. The sample is divided equally into four subgroups or panels called rotation groups. For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample and replaced by new dwellings from the same PSU or the next PSU on the list.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In general, imputation is used for item non-response (i.e. blanks within the questionnaire) and edit failures (i.e. invalid or inconsistent responses).

The Crime Survey for England and Wales (CSEW) asks a sole adult in a random sample of households about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked, covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS). These variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range was also selected from the household and asked about their experience of crime and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.

The Telephone-operated Crime Survey for England and Wales (TCSEW) became operational on 20 May 2020. It was a replacement for the face-to-face CSEW, which was suspended on 17 March 2020 because of the coronavirus (COVID-19) pandemic. It was set up with the intention of measuring the level of crime during the pandemic. As the pandemic continued throughout the 2020/21 survey year, questions have been raised as to whether the year ending March 2021 TCSEW is comparable with estimates produced in earlier years by the face-to-face CSEW. The ONS Comparability between the Telephone-operated Crime Survey for England and Wales and the face-to-face Crime Survey for England and Wales report explores those factors that may have a bearing on the comparability of estimates between the TCSEW and the former CSEW. These include survey design, sample design, questionnaire changes and modal changes.

More general information about the CSEW may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page.

History - the British Crime Survey

The CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this.

Secure Access CSEW data
In addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).

New methodology for capping the number of incidents from 2017-18
The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’.

Latest edition information

For the second edition (November 2020), the correct version of the 2018-2019 children non-victim form data has been deposited, previously the 2017-2018 data was made available.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/.

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2). 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through GeoPoll. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sampling quota of at least 200 observations per each Administrative 1 areas is set Exclusions: NA Design effect: NA

Mode of data collection

Computer Assisted Telephone Interview [CATI]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as NA. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

Since the population with access to mobile telephones is likely to differ from the rest of the population with respect to their access to food, post-hoc adjustments were made to control for the potential resulting bias. Post-stratification weights were built to adjust the sample distribution by gender and education of the respondent at admin-1 level, to match the same distribution in the total population. However, an additional step was needed to try to ascertain the food insecurity condition of those with access to phones compared to that of the total population.

Using FIES data collected by FAO through the GWP between 2014 and 2019, and a variable on access to mobile telephones that was also in the dataset, it was possible to compare the prevalence of food insecurity at moderate or severe level, and severe level only, of respondents with access to a mobile phone to that of the total population at national level.

The Law Enforcement Management and Administrative Statistics (LEMAS) survey collects data from a nationally representative sample of general-purpose agencies (i.e., local and county police departments, sheriffs' offices, and primary state police agencies). The 2020 LEMAS sample design called for the survey questionnaire to be sent to 3,499 general purpose law enforcement agencies, including 2,631 local and county police departments, 819 sheriffs' offices, and the 49 primary state police departments (Hawaii does not have a primary state police agency). The design called for all agencies employing 100 or more full-time equivalent sworn personnel to be included with certainty (self-representing), and for smaller agencies to be sampled from strata based on number of full-time equivalent sworn officers and type of agency. A total of 37 local police departments were determined to be out-of-scope for the survey because they had closed, had less than one full-time equivalent sworn officer, had contracted out their services with another law enforcement agency, or only had special enforcement responsibilities. The final mail out total of 3,462 agencies included 2,611 local police departments, 802 sheriffs' offices, and the 49 state agencies.