To this day, the Geodatindustry database is the world's most complete and accurate in the retail, commercial and industry area, with 25 years of experience and a qualified teams.

Geodatindustry Database is the perfect tool to lead your decision making, market analytics, strategy building, prospecting, advertizing compaigns, etc.

By purchasing this dataset, you gain access to more than 18,000 shopping malls all over the World, hosting millions of stores and welcoming millions of visitors each year.

Included Points of Interest in this dataset : -Shopping Malls and Centers -Outlets -Big Supermakets and Hypermarkets.

Information (if known) : shopping mall's name, physical address, number of shops, x,y coordinates, annual visitors counts (in millions), owner and managers, global area and GLA (in ranges), the website.

Global area and GLA Ranges : A = 0-2 500 m² B = 2 500-5 000 m² C = 5 000-10 000 m² D = 10 000-25 000 m²
E = 25 000-50 000 m² F = 50 000-75 000 m² G = 75 000-100 000 m² H = 100 000-1M m² I = 1M-10M m² J = 10M m² and +

Prices depend on the amount of Shopping Malls for each country. It goes from 59€ to 3990€ per country.

In July 2025, the visits to indoor malls in the United States compared to the previous year increased by *** percent. This growth was especially high in May, at *** percent. Visits to open-air shopping centers and outlet malls also peaked in April and May 2025, while the lowest number of visits were observed in February.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

This statistic shows the growth rate of the number of shopping malls worldwide from 2012 to 2016, by region. From 2012 to 2016, the number of shopping centers in Europe was forecast to grow at a CAGR of *** percent.

Our dataset gives access to the most precise data thanks to the power of our advanced algorithms. We use massive, precise and representative geolocation data from mobile applications that we aggregate, standardize and couple with manual counts to offer the most reliable analysis. This data product contains footfall data as well as shopping center names, city, postal code and geographies for shopping centers in Belgium / England / France / Germany / Italy / Netherlands / Spain, over the past several years. Use Cases: Foot Traffic Analytics Foot Traffic Analytics Territory Planning Gain detailed insights into pedestrian traffic across diverse locations, such as addresses, shopping centers, and shopping areas, to make strategic decisions for your location strategy. Identify high-traffic areas to optimize site selection and expansion plans. Competition Analytics Benchmark footfall within the shopping centers of your competitors, enabling informed business decisions. Understand competitor performance and identify opportunities for market share growth by analyzing comparative traffic patterns. Marketing Targeting Enhance your marketing strategies by analyzing footfall data to identify high-traffic areas and peak times. Target your marketing and promotional efforts more effectively by understanding where and when to reach your audience, maximizing engagement and conversion rates.. Urban and Infrastructure Planning Support urban and infrastructure planning by providing data on pedestrian traffic flows. Help city planners and developers design more efficient public spaces, transportation hubs, and commercial areas by understanding how people move through different environments.

In 2017, there were approximately ******* shopping malls spread across the United States. Back in 1970, there were only ****** shopping malls in the United States. A shopping mall or center is typically a covered area which contains shops and restaurants, as well as other retail establishments which people can walk between. Woodbury Common Premium Outlets in Central Valley, New York was the leading shopping mall in the United States based on sales per square foot in 2017. Retail in the U.S. Total retail sales in the United States were projected to amount to **** trillion U.S. dollars in 2022, up from *** trillion U.S. dollars in 2018. Retail establishments come in many forms such as grocery stores, restaurants, bookstores, and shopping malls. There are around **** million retail establishments in the United States. Do American consumers still shop at malls? While the United States is not home to the largest shopping mall in the world, it was the leading country for per capita shopping center retail sales. Americans continue to shop at malls, instead of switching to online shopping, as malls are seen as a one-stop shopping experience where one can visit multiple retailers and make several purchases at one location. Another attraction of malls for U.S. shoppers is that a trip to the mall often takes the form of an outing with friends and family.

The dataset contains locations and attributes for Shopping Centers, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies.

Xtract.io's comprehensive location data for European malls provides international retail strategists. Researchers, investors, and business developers can utilize this dataset to analyze retail landscape, identify market trends, and develop sophisticated strategies for European shopping center markets.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geo-spatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

This dataset captures observations of consumer visits to three major shopping malls in Johannesburg, South Africa, from 2022 to 2023. The data, sourced from Fetch Analytics, utilizes ​​​​smartphone​​​​ signal tracking to provide insights into consumer behavior. Key variables include mall name, visit frequency, distance traveled, and demographic indicators such as income and Living Standard Measure (LSM). The dataset allows for a granular analysis of how spatial and socioeconomic factors influence shopping patterns in a fragmented retail landscape. This dataset is valuable for researchers investigating consumer behavior, spatial economics, and urban retail planning.

Dataset Descriptions This analysis involves three main datasets—Sales Data, Customer Data, and Shopping Mall Data—which provide information on transactions, customer demographics, and shopping mall characteristics. Each dataset contributes unique aspects that, when combined, offer valuable insights into sales patterns, customer behavior, and the impact of mall features on sales.

Sales Data: This dataset records transaction-level details for products sold across shopping malls. Key columns include:

invoice_no: Unique identifier for each transaction. customer_id: Identifier for the customer making the purchase. category: Product category (e.g., Clothing, Shoes). quantity: Quantity of each product purchased. invoice date: Date of transaction. price: Price of each product purchased. shopping_mall: Mall where the transaction took place. Purpose: Analyzing this dataset allows us to understand product sales across different malls and track how sales change over time or by category.

Customer Data: This dataset provides demographic details for each customer, including:

customer_id: Unique identifier for each customer. gender: Customer’s gender. age: Customer’s age. payment_method: Preferred payment method for transactions. Purpose: This dataset supports customer segmentation by demographics, such as age and gender, and helps identify spending patterns and payment preferences.

Shopping Mall Data: This dataset contains details of various shopping malls in California where the transactions occur. The columns include:

shopping_mall: Name of the mall. construction_year: Year the mall was established. area_sqm: Total area of the mall in square meters. location: City in California where the mall is located. stores_count: Number of stores within the mall. Purpose: This dataset provides context on mall attributes and enables analysis of how mall features—such as size, store count, and location—might influence customer traffic, sales, and purchasing behaviors.

Goal of Analysis The goal of analyzing this data is to uncover patterns and insights that can inform decisions for optimizing sales strategies, enhancing customer engagement, and understanding the effects of mall characteristics on customer behavior. By exploring connections among sales performance, customer demographics, and mall attributes, this analysis seeks to answer questions like:

Which mall characteristics (e.g., size, age, store count) are most strongly associated with higher sales volumes? How do customer demographics, such as age and gender, impact spending patterns across malls? What product categories are more popular in specific malls, and how does this vary with mall characteristics?

Expected Outcomes With this analysis, we aim to develop actionable insights into the sales dynamics in California's shopping malls, identify customer preferences by mall characteristics, and understand how mall attributes drive retail success. These insights can be valuable for mall operators, retailers, and marketing teams looking to improve customer experience, tailor product offerings, and maximize sales performance across different mall locations.

Shopping Center Survey: Sales: GBA: 24 Districts: Clothes and Sports Accessories data was reported at 587,412.002 ARS th in Aug 2019. This records an increase from the previous number of 584,012.412 ARS th for Jul 2019. Shopping Center Survey: Sales: GBA: 24 Districts: Clothes and Sports Accessories data is updated monthly, averaging 38,989.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 680,668.026 ARS th in Dec 2018 and a record low of 2,044.000 ARS th in Jan 2002. Shopping Center Survey: Sales: GBA: 24 Districts: Clothes and Sports Accessories data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

The locations of the shopping centers within Fairfax County.

CAP’s Premium USA Shopping Centers Dataset tracks 43K+ Shopping Centers and includes all features of the Basic Dataset, plus exclusive premium variables. These additions provide deeper insights, enabling more granular analysis for enhanced decision-making in retail, real estate, and market research.

Shopping Center Survey: Sales: GBA: 24 Districts: Leisure & Amusement data was reported at 308,030.869 ARS th in Aug 2019. This records a decrease from the previous number of 591,331.981 ARS th for Jul 2019. Shopping Center Survey: Sales: GBA: 24 Districts: Leisure & Amusement data is updated monthly, averaging 17,427.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 591,331.981 ARS th in Jul 2019 and a record low of 4,078.000 ARS th in Nov 2001. Shopping Center Survey: Sales: GBA: 24 Districts: Leisure & Amusement data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

Defined area where retail development is concentrated (generally comprising the Primary and those Secondary Frontages which are adjoining and closely related to the primary shopping frontage).

Shopping Center Survey: Sales: GBA: 24 Districts data was reported at 4,407,421.928 ARS th in Aug 2019. This records a decrease from the previous number of 5,104,718.612 ARS th for Jul 2019. Shopping Center Survey: Sales: GBA: 24 Districts data is updated monthly, averaging 407,704.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 5,367,671.920 ARS th in Dec 2018 and a record low of 37,821.000 ARS th in Jan 2002. Shopping Center Survey: Sales: GBA: 24 Districts data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

Shopping Center Survey: Sales: GBA: 24 Districts: Textiles, Footwear & Garment data was reported at 1,432,661.625 ARS th in Aug 2019. This records a decrease from the previous number of 1,703,933.691 ARS th for Jul 2019. Shopping Center Survey: Sales: GBA: 24 Districts: Textiles, Footwear & Garment data is updated monthly, averaging 154,545.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 2,407,475.813 ARS th in Dec 2018 and a record low of 15,184.000 ARS th in Jan 2002. Shopping Center Survey: Sales: GBA: 24 Districts: Textiles, Footwear & Garment data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

Shopping Center Survey: Sales: GBA: 24 Districts: Fast Food Floor & Kiosks data was reported at 654,024.737 ARS th in Aug 2019. This records a decrease from the previous number of 831,385.343 ARS th for Jul 2019. Shopping Center Survey: Sales: GBA: 24 Districts: Fast Food Floor & Kiosks data is updated monthly, averaging 41,263.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 831,385.343 ARS th in Jul 2019 and a record low of 5,638.000 ARS th in Jan 2002. Shopping Center Survey: Sales: GBA: 24 Districts: Fast Food Floor & Kiosks data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

Shopping Center Survey: Sales: Greater Buenos Aires: Textiles, Footwear & Garment data was reported at 3,563,543.509 ARS th in Aug 2019. This records a decrease from the previous number of 4,041,777.971 ARS th for Jul 2019. Shopping Center Survey: Sales: Greater Buenos Aires: Textiles, Footwear & Garment data is updated monthly, averaging 366,043.000 ARS th from Apr 2000 (Median) to Aug 2019, with 233 observations. The data reached an all-time high of 5,537,863.136 ARS th in Dec 2018 and a record low of 31,830.000 ARS th in Jan 2002. Shopping Center Survey: Sales: Greater Buenos Aires: Textiles, Footwear & Garment data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.H003: Shopping Centre Survey: Sales: Old Methodology.

Business Analyst Layer: Shopping Centers & Malls