Sample data in ESRI Shapefile format available for download for testing purposes.

In 2020, a total of over ** percent of consumers across the globe shopped online: reaching nearly ** percent each, the leading regions that year were South America and Asia. North America had the lowest share with just over ***** in **** consumers buying items on the internet. The online store that was used most frequently by shoppers worldwide was Amazon.com.

Favorite online stores in the U.S. As of November 2020, an estimated ** percent of U.S. consumers stated that their online shop of choice was Amazon, making it by far the favorite e-commerce shop among online shoppers. With less than ** percent, Walmart’s web shop ranked second. Both male and female consumers in the country had a clear preference for Amazon, however, certain online stores were more popular among specific genders. For instance, more men liked visiting eBay, while a higher percentage of women had a preference for Target.

Why do consumers like Amazon? There were various reasons why U.S. shoppers used Amazon to buy products in 2020, the leading reason being the fast and free shipping services provided. Other key factors consumers mentioned, included Amazon’s broad selection, the easy return process, and the platform having some of the lowest prices.

Data description “e-shop clothing 2008”

Variables:

1. YEAR (2008)

========================================================

1. MONTH -> from April (4) to August (8)

========================================================

1. DAY -> day number of the month

========================================================

1. ORDER -> sequence of clicks during one session

========================================================

1. COUNTRY -> variable indicating the country of origin of the IP address with the following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

1. SESSION ID -> variable indicating session id (short record)

========================================================

1. PAGE 1 (MAIN CATEGORY) -> concerns the main product category: 1-trousers 2-skirts 3-blouses 4-sale

========================================================

1. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product (217 products)

========================================================

1. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

1. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

1. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

1. PRICE -> price in US dollars

========================================================

1. PRICE 2 -> variable informing whether the price of a particular product is higher than the average price for the entire product category

1-yes 2-no

========================================================

1. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

I want to know how to solve this data regarding any problem (clustering, regression, classification, EDA)

Source: https://archive.ics.uci.edu/ml/datasets/clickstream+data+for+online+shopping

TRCA GIS Open data on ArcGIS online. This link will take you to an external site URL: https://trca-camaps.opendata.arcgis.com/

This parcels data set is a spatial representation of municipal tax lots for Mercer County, New Jersey that have been extracted from the NJ statewide parcels composite by the NJ Office of Information Technology, Office of GIS (NJOGIS). Parcels at county boundaries have been modified to correspond with the NJ county boundaries and the parcels in adjacent counties.Each parcel contains a field named PAMS_PIN based on a concatenation of the county/municipality code, block number, lot number and qualification code. Using the PAMS_PIN, the dataset can be joined to the MOD-IV database table that contains supplementary attribute information regarding lot ownership and characteristics. Due to irregularities in the data development process, duplicate PAMS_PIN values exist in the parcel records. Users should avoid joining MOD-IV database table records to all parcel records with duplicate PAMS_PINs because of uncertainty regarding whether the MOD-IV records will join to the correct parcel records. There are also parcel records with unique PAMS_PIN values for which there are no corresponding records in the MOD-IV database tables. This is mostly due to the way data are organized in the MOD-IV database.The polygons delineated in the dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such.The MOD-IV (Tax Assessor's) table for the county is packaged together with the parcels as one download. The MOD-IV system provides for uniform preparation, maintenance, presentation and storage of property tax information required by the Constitution of the State of New Jersey, New Jersey Statutes and rules promulgated by the Director of the Division of Taxation. MOD-IV maintains and updates all assessment records and produces all statutorily required tax lists for property tax bills. This list accounts for all parcels of real property as delineated and identified on each municipality's official tax map, as well as taxable values and descriptive data for each parcel. Tax List records were received as raw data from the Taxation Team of NJOIT which collected source information from municipal tax assessors and created the statewide table. This table was subsequently processed for ease of use with NJ tax parcel spatial data and split into an individual table for each county.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

In 2022, global consumer preferences concerning the purchase of apparel and footwear were somewhat evenly split. About ***** in ten consumers around the world said they were more likely to buy clothing and shoes online, while only a slightly higher number of respondents stated they more commonly bought such goods in-store. Winning only by a couple of percentage points, the modest majority said they use a healthy mix of both channels.

A shapefile of 311 undersea features from all major oceans and seas has been created as an aid for retrieving georeferenced information resources. The geographic extent of the shapefile is 0 degrees E to 0 degrees W longitude and 75 degrees S to 90 degrees N latitude. Many of the undersea features (UF) in the shapefile were selected from a list assembled by Weatherall and Cramer (2008) in a report from the British Oceanographic Data Centre (BODC) to the General Bathymetric Chart of the Oceans (GEBCO) Sub-Committee on Undersea Feature Names (SCUFN). Annex II of the Weatherall and Cramer report (p. 20-22) lists 183 undersea features that "may need additional points to define their shape" and includes online links to additional BODC documents providing coordinate pairs sufficient to define detailed linestrings for these features. For the first phase of the U.S. Geological Survey (USGS) project, Wingfield created polygons for 87 of the undersea features on the BODC list, using the linestrings as guides; the selected features were primarily ridges, rises, trenches, fracture zones, basins, and seamount chains. In the second phase of the USGS project, Wingfield and Hartwell created polygons for an additional 224 undersea features, mostly basins, abyssal plains, and fracture zones. Because USGS is a Federal agency, the attribute tables follow the conventions of the National Geospatial-Intelligence Agency (NGA) GEOnet Names Server (http://earth-info.nga.mil/gns/html).

Dataset from Urban Redevelopment Authority. For more information, visit https://data.gov.sg/datasets/d_d14da225fccf921049ab64238ff473d9/view

The online shopping dataset consists of data from an e-commerce platform that sells clothes for pregnant women. The data has been collected over five months, from april to August 2008 and consists of 165474 entries (rows) and 14 columns, with each column representing a feature.

The features are:

• Year: It represents the year in which the data is collected, which is 2008
• Month: the features indicates the month when data was recorded from April (4) to August (9)
• Day: It shows the specific day in the month, ranging from 1 to 31
• Order: Sequence of clicks during one session
• Country: The country from which the website was accessed were Australia, Austria and 45 other places
• Session ID: Indicates the user sessions
• Page 1 (Main Category): The feature represents the category of products like trousers , skirts, blouses and other products that are on sale represented by 1, 2, 3, and 4.
• Page 2 (Clothing Model): The variable represents the code for each product. The dataset has a total of 217 products
• Color: the explanatory variable states the colour of the product. The products are available in a total of 14 colours for the clothing items
• Location: The variable indicates the location of the product in the webpage. The screen is divided in 6 parts, such that 3 products are displayed in the top left, right, and middle, and 3 products are displayed in the bottom left, right and middle
• Model Photography: The feature states how the product was photographed. Products are photographed in two fashions, namely face-to-face, which means that the picture was taken full-on where the products directly faces the camera. The other type is profile, where the picture was taken from the side, indicating the outline of the product
• Price: Indicates the price of the specific product in US Dollars.
• Price 2: The variable states if the price of the product is greater than the average price of the category to which it belongs.
• Page: Specifies the page number of the store website, which ranges from 1 to 5

This map is one of a series of soil landscape maps that are intended for all of eastern and central NSW, based on standard 1:100,000 or 1:250,000 topographic sheets. The map provides an inventory of soil and landscape properties of the Blackville area and identifies major soil and landscape qualities and constraints. It integrates soil and topographic features into single units with relatively uniform land management requirements. Soils are described in terms of soil materials in addition to Australian Soil Classification and Great Soil Group systems. Related Datasets: The dataset area is also covered by the mapping of the Soil and Land Resources of the Liverpool Plains Catchment and Hydrogeological landscapes of NSW. Online Maps: This and related datasets can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Banks RG, 1998, Soil Landscapes of the Blackville 1:100,000 Sheet map and report, NSW Department of Land and Water Conservation, Sydney.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

These data-sets are polygon shapefiles that represent flood inundation boundaries for 157 flooding scenarios in an 8-mile reach of the Papillion Creek near Offutt Air Force Base. These shapefiles were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force, Offutt Air Force Base for use within the USGS Flood Inundation Mapping program. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages on the Papillion Creek at Fort Crook, Nebr. (station 06610795) and Papillion Creek at Harlan Lewis Road near La Platte, Nebr. (station 06610798). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System web interface at https://doi.org/10. ...

Linn County, Iowa hydrology information available for download in shapefile format.Layers include:StreamsWaterbodies2008 Flooded AreaUpdate FrequencyApproximately once a weekAdditional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps, apps, and data of interest from various County departments.Visit the Official FEMA National Flood Hazard Layer web map on the web.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This resource is a member of a series. 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) System (MTS). The MTS 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. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of states for the purpose of data presentation.

This document contains step by step guidance for adding Shapefile to ArcGIS Hub through ArcGIS Online.

Complete Topographic dataset in shapefile format. Consume this dataset if you wish to download the entire Topographic dataset at once.

Linn County, Iowa contours clipped to the approximate area of PLSS Township 83 Range 6. This dataset is in shapefile (.shp) format.This dataset is updated as needed, and not on a regular basis.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps and apps of interest from various departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This is a resource that will help me learn how to make a WMS thing. This is a resource that will help me learn how to make a WMS thing. This is a resource that will help me learn how to make a WMS thing. This is a resource that will help me learn how to make a WMS thing. This is a resource that will help me learn how to make a WMS thing.

Nearly ** percent of American parents of Gen Alpha children did not allow their children to shop online independently in 2024. Engaging in e-commerce unsupervised was only allowed for a small minority of children born from 2010. Streaming into their world Still under the watchful eyes of their parents, Generation Alpha is molding a digital identity through preferred social platforms. YouTube stands out as the favorite, accounting for nearly **** of their total platform usage. TikTok is also making headway among this age group, though its screen time still falls behind, less than **** of YouTube’s. On average, Gen Alpha spends almost *** hours a day watching YouTube videos and about ****************** scrolling through TikTok, illustrating a daily rhythm increasingly shaped by video-based entertainment. Advertising reach and purchase behavior The influence of video platforms makes a difference in reaching a big chunk of this demographic, with at least ** percent of children having already seen advertisements on YouTube. Showing a strong correlation between media exposure and consumer behavior, with toys, clothing, and electronics ranking as top purchases. These trends suggest that while Gen Alpha may not have full shopping autonomy, their preferences are already being shaped significantly by the media they consume and the ads they encounter.

Dataset from Urban Redevelopment Authority. For more information, visit https://data.gov.sg/datasets/d_6d6fd6505f23d7fb90dec567afd555bb/view