United States Construction VIP: Private: Commercial: MR: Shopping Mall data was reported at 0.300 USD bn in May 2018. This records an increase from the previous number of 0.271 USD bn for Apr 2018. United States Construction VIP: Private: Commercial: MR: Shopping Mall data is updated monthly, averaging 0.206 USD bn from Jan 1993 (Median) to May 2018, with 305 observations. The data reached an all-time high of 0.406 USD bn in Jul 2008 and a record low of 0.092 USD bn in Dec 2010. United States Construction VIP: Private: Commercial: MR: Shopping Mall data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EA001: Value of Construction Put in Place (VIP): Current Price.

This product provides daily, aggregated foot traffic counts at the retail center level, offering comprehensive coverage across over 30,000 retail centers in the United States.

Each mall or retail center is meticulously categorized by type, such as super-regional, power, or lifestyle center, and includes Gross Leasable Area (GLA). This enables robust, structured analysis across various formats and geographical regions.

Distinct from datasets that aggregate tenant-level activity, this product precisely measures the unique number of visits to the retail center itself. It is ground truth validated against physical hardware sensors, ensuring highly accurate measurement even in complex, built-up, and multi-level environments where mobile-only data sources often falter.

Mall-level traffic data can be utilized independently for broad market insights or alongside store-level visit data to understand how individual tenants are performing relative to overall center trends. The data is fully aggregated and anonymized, delivered as a daily feed to support critical business functions such as benchmarking, thorough lease evaluations, and in-depth long-term trend analysis.

United States Retail Sales: Furniture and Home Furnishings Stores (FH) data was reported at 10.166 USD bn in Oct 2018. This records an increase from the previous number of 9.928 USD bn for Sep 2018. United States Retail Sales: Furniture and Home Furnishings Stores (FH) data is updated monthly, averaging 7.615 USD bn from Jan 1992 (Median) to Oct 2018, with 322 observations. The data reached an all-time high of 11.636 USD bn in Dec 2017 and a record low of 3.846 USD bn in Jan 1992. United States Retail Sales: Furniture and Home Furnishings Stores (FH) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Miscellaneous Stores Retail data was reported at 11.376 USD bn in Jun 2018. This records a decrease from the previous number of 12.174 USD bn for May 2018. United States Retail Sales: Miscellaneous Stores Retail data is updated monthly, averaging 8.662 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 12.350 USD bn in Dec 1999 and a record low of 3.642 USD bn in Jan 1992. United States Retail Sales: Miscellaneous Stores Retail data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Book Stores data was reported at 1.320 USD bn in Aug 2018. This records an increase from the previous number of 661.000 USD mn for Jul 2018. United States Retail Sales: Book Stores data is updated monthly, averaging 1.022 USD bn from Jan 1992 (Median) to Aug 2018, with 320 observations. The data reached an all-time high of 2.425 USD bn in Aug 2008 and a record low of 523.000 USD mn in Apr 1992. United States Retail Sales: Book Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: sa: Miscellaneous Stores Retail data was reported at 10.994 USD bn in Jun 2018. This records an increase from the previous number of 10.973 USD bn for May 2018. United States Retail Sales: sa: Miscellaneous Stores Retail data is updated monthly, averaging 8.743 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 11.138 USD bn in Jan 2018 and a record low of 4.292 USD bn in Mar 1992. United States Retail Sales: sa: Miscellaneous Stores Retail data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Shoe Stores data was reported at 3.072 USD bn in May 2018. This records an increase from the previous number of 2.829 USD bn for Apr 2018. United States Retail Sales: Shoe Stores data is updated monthly, averaging 2.053 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 4.268 USD bn in Dec 2016 and a record low of 1.161 USD bn in Feb 1993. United States Retail Sales: Shoe Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: Hobby, Toy and Game Stores data was reported at 1.358 USD bn in May 2018. This records an increase from the previous number of 1.286 USD bn for Apr 2018. United States Retail Sales: Hobby, Toy and Game Stores data is updated monthly, averaging 1.094 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 4.073 USD bn in Dec 1996 and a record low of 576.000 USD mn in Feb 1993. United States Retail Sales: Hobby, Toy and Game Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

United States Retail Sales: BM: ow: Hardware Stores data was reported at 2.240 USD bn in Sep 2018. This records a decrease from the previous number of 2.452 USD bn for Aug 2018. United States Retail Sales: BM: ow: Hardware Stores data is updated monthly, averaging 1.507 USD bn from Jan 1992 (Median) to Sep 2018, with 321 observations. The data reached an all-time high of 2.826 USD bn in May 2018 and a record low of 805.000 USD mn in Feb 1993. United States Retail Sales: BM: ow: Hardware Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H001: Retail Sales: By NAIC System.

This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

To create this layer, OCTO staff used ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, staff reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a additional examples that meet, or come very close to, the full-service grocery store criteria. The explanation from Office of Planning regarding how the agency created their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”Staff also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

This dataset contains measures of the number and density of grocery stores – including supermarkets, specialty food stores, and warehouse clubs – per United States census tract from 2003 through 2017. These types of businesses represent places where neighborhood residents can obtain fresh and healthy foods.

Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets (also called supercenters), discount department stores, and grocery stores in the United States, headquartered in Bentonville, Arkansas. The company was founded by Sam Walton in nearby Rogers, Arkansas in 1962 and incorporated under Delaware General Corporation Law on October 31, 1969. It also owns and operates Sam's Club retail warehouses. In India, Walmart operates under the name of Flipkart Wholesale.

As of July 31, 2022, Walmart has 10,585 stores and clubs in 24 countries, operating under 46 different names. Out of which we have chosen 45 stores for basic analysis.

Walmart is the world's largest company by revenue, with about US$570 billion in annual revenue, according to the Fortune Global 500 list in May 2022.

How Walmart uses Big Data?

• Improving Store Checkout: By using Predictive Analysis, the stores can anticipate demand at a certain week and determine how many Sales Representatives / Employees are needed.
• Managing the Steps of Supply Chain: The company optimizes the routes to the shipping dock and tracks the number of times the product is accessed before it reaches the Customer's destination. Also, it uses the data to analyze transportation lanes and routes for the company's trucks. These data help Walmart keep transportation costs down and schedule an appropriate time for drivers.
• Optimizing Product Assortment: By analyzing customer preferences and shopping patterns, Walmart accelerates the decision-making on how to maintain stocks. Big Data provides insights on new items and discontinued products.
• Personalizing Shopping Experience: With Big Data, Walmart analyzes the shopping preferences of the customers to develop a consistent and delightful shopping experience. and much more...

Small specialty retail stores are influenced by broad macroeconomic variables rather than product-specific trends. Still, individual segments do respond to specific shifts in consumer preferences. In recent years, rising per capita disposable income has sustained demand throughout the retail sector. A recovery from the pandemic boosted consumer spending and encouraged consumers to return to brick-and-mortar stores. Specialty retailers were relatively unaffected by pandemic declines as high-income consumers and tobacco users, two significant markets for the industry, continued to spend. Competition from online and big-box retailers has risen, putting downward pressure on profit. More stores are expanding their online platforms to boost consumer reach and provide additional revenue streams. Rising operational costs have contributed to a slight dip in profit. Revenue for small specialty retailers is expected to swell at a CAGR of 4.0% to $68.4 billion through the end of 2025, including a hike of 2.0% in 2025 alone. Despite intensifying competition from discount department stores and online retailers, specialty retail stores have relied on serving a particular niche to remain successful. Big-box stores offer a one-stop shopping experience with lower prices for similar products. External competition has driven underperforming retailers to exit the industry, leaving nonemployers and small retail stores with low barriers to entry. Still, revenue gains have prompted the emergence of many new specialty retailers seeking to capitalize on the trend of shopping locally and broader sustainability trends. Small retailers have maintained a strong customer base by offering a unique in-store experience and high-quality products. Moving forward, small specialty retailers will continue expanding, albeit slower than in the previous five-year period. A gain in consumer spending and consumer confidence compounded by growing environmental awareness will support specialty retail store sales. Ongoing competition from large-scale retailers and declining smoking rates will mitigate specialty retailers' expansion. More consumers view consumer products, particularly luxury and nostalgic items, as sound investment options. Stores can benefit from this trend by stocking high-end goods that appeal to these consumers, focusing on popular brands. Revenue is expected to expand at a CAGR of 1.4% to $73.3 billion through the end of 2030.

In 2023, consumers in the United States were surveyed about their regular food and everyday products shopping destinations. Among those who shopped at discount stores, ** percent of Millennials reported doing so, whereas the corresponding share for Gen Z was only ** percent. Find this and more survey data in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

Graph and download economic data for Advance Retail Sales: Furniture and Home Furnishings Stores (RSFHFS) from Jan 1992 to May 2025 about furniture, retail trade, sales, retail, and USA.

Graph and download economic data for Monthly State Retail Sales: Furniture and Home Furnishings Stores in the United States (MSRSUSA442) from Jan 2019 to Feb 2025 about furniture, retail trade, sales, retail, housing, and USA.

United States Retail Inv: sa: Food & Beverage Stores data was reported at 48.461 USD bn in May 2018. This records an increase from the previous number of 48.182 USD bn for Apr 2018. United States Retail Inv: sa: Food & Beverage Stores data is updated monthly, averaging 33.529 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 49.223 USD bn in Feb 2018 and a record low of 26.859 USD bn in Mar 1992. United States Retail Inv: sa: Food & Beverage Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.C022: Retail Inventories: By NAIC System.

United States Retail Sales: sa: Sporting Goods, Hobby, Book and Music Stores (SG) data was reported at 6.669 USD bn in Jun 2018. This records a decrease from the previous number of 6.891 USD bn for May 2018. United States Retail Sales: sa: Sporting Goods, Hobby, Book and Music Stores (SG) data is updated monthly, averaging 6.192 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 7.601 USD bn in Jun 2016 and a record low of 3.385 USD bn in Jan 1992. United States Retail Sales: sa: Sporting Goods, Hobby, Book and Music Stores (SG) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.