This statistic shows the number of adult grocery app users in the United States from 2017 to 2022. By 2022, the number of adult grocery app users in the U.S. is projected to reach 30.4 million consumers.

In 2024, Instacart was the most downloaded mobile application for grocery delivery in the United States, with over ***** million downloads nationwide. 7-Eleven ranked second, with roughly *** million downloads, followed closely by Gopuff, with *** million. InstaCart: the good and not so good Not only was InstaCart the most downloaded grocery delivery app in the U.S. through most of 2024, but in December 2023, the third-party platform was also the ******-most visited grocery website in the country, with a visit share of over ***** percent. However, when it comes to grocery delivery brand awareness, InstaCart barely cracks the top four, as it finds itself behind the likes of major retail chains Walmart and Costco, as well as competitor Uber Eats. Big-box retailers vs. third-party platforms When asked their preferred online food and grocery providers in the United States, more than ** percent of respondents listed big-box retailers, while only ** percent listed third-party platforms. Brand awareness and consumer preferences translate to sales, and as a result, the leading online grocery companies in terms of sales share are nearly all big-box retailers, with Walmart leading the way.

A 2021 survey revealed that online grocery shopping in Mexico was most commonly done via the web, regardless of the shoppers' age. However, app usage for online food and beverage ordering was slightly more popular among younger consumers.

In 2024, the iOS Tesco app, Tesco Groceries for iPhone, was the most downloaded grocery shopping app in the United Kingdom, with over *****million downloads. The Sainsbury's Groceries app ranked second with **million downloads, followed by the Gopuff app with about ****thousand downloads.

Lidl Plus was the most downloaded supermarket app in the Netherlands in April of 2024. The German discounter's mobile application was downloaded over ******* times in April, leading this month's ranking by ****** downloads compared to the runner-up Albert Heijn.

In 2024, the top three most downloaded grocery delivery apps were all quick commerce companies hailing from India. Zepto's mobile application was by far the most downloaded grocery delivery app with nearly ** million app downloads globally. Blinkit and bigbasket ranked second and third, with **** million and **** million app annual app downloads, respectively.

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

Online Grocery Market Outlook

According to our latest research, the global online grocery market size reached USD 420.6 billion in 2024, reflecting a robust expansion fueled by evolving consumer preferences and rapid digitalization. The market is projected to grow at a CAGR of 18.2% from 2025 to 2033, reaching a forecasted value of USD 1,921.8 billion by 2033. This remarkable growth is primarily attributed to increasing urbanization, widespread adoption of smartphones, and the growing demand for convenience in grocery shopping. The online grocery market continues to transform the retail grocery landscape, with innovative technologies and changing lifestyles driving widespread adoption across all demographics.

One of the most significant growth factors for the online grocery market is the increasing penetration of high-speed internet and smartphones globally. As more consumers gain access to reliable connectivity and user-friendly mobile applications, the barriers to online grocery shopping are rapidly diminishing. This digital transformation has enabled retailers to reach a broader customer base, offering seamless shopping experiences that were previously unattainable through traditional brick-and-mortar channels. Additionally, the integration of advanced technologies such as AI-driven recommendation engines, real-time inventory updates, and secure payment gateways has enhanced user engagement and trust, further propelling market growth.

The shift in consumer behavior toward convenience and time efficiency is another critical driver for the online grocery market. Modern consumers, particularly in urban environments, are increasingly seeking solutions that save time and reduce the hassle of in-store shopping. The ability to order groceries online and have them delivered directly to one’s doorstep, or to schedule a convenient click-and-collect pickup, has become a significant value proposition. This trend has been further accelerated by the COVID-19 pandemic, which heightened awareness of contactless shopping and health safety, leading to a surge in first-time online grocery shoppers and increased frequency of repeat purchases.

Strategic partnerships and investments by major retailers and technology companies are also catalyzing the expansion of the online grocery market. Established supermarket chains, e-commerce giants, and even startups are investing heavily in logistics infrastructure, last-mile delivery solutions, and digital marketing. These investments are not only improving service levels but are also enabling competitive pricing and a wider assortment of products. Moreover, the rise of subscription-based grocery delivery services and the integration of loyalty programs are fostering customer retention and increasing average order values, thereby contributing to sustained market growth.

Regionally, the online grocery market exhibits dynamic growth patterns, with Asia Pacific leading the charge due to its large population, rising disposable incomes, and rapid urbanization. North America and Europe are also witnessing substantial adoption, driven by tech-savvy consumers and high internet penetration rates. Emerging markets in Latin America and the Middle East & Africa are catching up, supported by investments in digital infrastructure and changing consumer lifestyles. Each region presents unique opportunities and challenges, influenced by local consumer preferences, regulatory environments, and logistical capabilities.

Product Type Analysis

The online grocery market, when segmented by product type, reveals significant variations in consumer preferences and purchase behaviors. Fresh produce has emerged as a high-growth segment, as consumers increasingly prioritize health and wellness in their dietary choices. Online platforms are responding by establishing partnerships with local farmers and suppliers, ensuring the availability of fresh fruits and vegetables with minimal delivery times. The integration of real-time inventory management and cold chain logistics has addressed traditional concerns rega

Grocery List Automation App Market Outlook

According to our latest research, the Grocery List Automation App market size reached USD 1.32 billion in 2024, with a robust compound annual growth rate (CAGR) of 18.7% observed between 2024 and 2025. Bolstered by rising smartphone penetration, increasing digital adoption in daily life, and heightened consumer focus on convenience and efficiency, the market is expected to continue its upward trajectory. By 2033, the global market is forecasted to reach USD 6.54 billion, reflecting sustained expansion across both developed and emerging economies as per our most recent analysis.

The primary growth factor for the Grocery List Automation App market is the accelerating integration of digital solutions into everyday consumer routines. As lifestyles become increasingly fast-paced, consumers are seeking tools that streamline mundane tasks, such as grocery shopping. Grocery list automation apps provide a seamless experience through features like real-time synchronization, smart suggestions, and integration with e-commerce platforms. The proliferation of smart devices and the rising comfort level with mobile applications further drive adoption, with consumers appreciating the ability to create, manage, and share lists effortlessly from their smartphones, tablets, or computers. Additionally, the growing trend of online grocery shopping, especially post-pandemic, has created a symbiotic relationship between automation apps and e-commerce platforms, further fueling market growth.

Another significant driver is the continuous evolution of app functionalities powered by artificial intelligence and machine learning. Modern grocery list automation apps are no longer limited to basic list management; they now offer advanced features such as personalized recommendations, voice integration, barcode scanning, and recipe-based list generation. These innovations enhance user engagement and retention by providing a tailored and intuitive experience. The integration with smart home devices, such as voice assistants and IoT-enabled refrigerators, adds another layer of convenience, making it easier for users to update their lists hands-free or receive automated reminders based on consumption patterns. This technological advancement is particularly appealing to tech-savvy consumers and younger demographics, who are more likely to embrace digital solutions for daily tasks.

The market is also benefiting from increased commercial adoption, particularly among retailers and grocery delivery services. Businesses are leveraging grocery list automation apps to improve customer engagement, streamline inventory management, and offer value-added services. For example, supermarkets and online grocers are integrating these apps with loyalty programs, personalized promotions, and automated reordering systems to enhance the shopping experience and foster customer loyalty. Moreover, the ability to analyze aggregated shopping data provides valuable insights into consumer behavior, enabling businesses to optimize product offerings and marketing strategies. As competition in the retail sector intensifies, the adoption of such digital tools is becoming a key differentiator, further propelling market growth.

Regionally, North America remains the largest and most mature market for grocery list automation apps, thanks to high smartphone penetration, advanced digital infrastructure, and a tech-savvy population. However, Asia Pacific is emerging as a significant growth engine, driven by rapid urbanization, rising disposable incomes, and increasing digital literacy. Europe also exhibits strong adoption rates, particularly in Western European countries where consumers are quick to embrace innovative digital solutions. While Latin America and the Middle East & Africa currently account for a smaller share of the global market, these regions are expected to witness accelerated growth over the forecast period as internet access and smartphone usage continue to rise.

Platform Analysis

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The online food and grocery retail market is experiencing robust growth, driven by the increasing adoption of e-commerce, changing consumer lifestyles, and the convenience offered by online platforms. Our analysis projects a market size of $750 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the rising popularity of online grocery shopping, particularly amongst younger demographics (less than 40 years old), is a major catalyst. Secondly, technological advancements, such as improved delivery logistics and user-friendly mobile applications, are enhancing the overall customer experience and driving market penetration. Thirdly, the increasing prevalence of subscription services and personalized recommendations are further boosting sales and fostering customer loyalty. Finally, the expansion of online grocery offerings into diverse categories, including fresh produce, frozen foods, and health-focused products, caters to evolving consumer preferences. Major players like Walmart, Amazon, and Kroger are actively investing in infrastructure and technology to maintain their competitive edge in this rapidly evolving market. Despite the significant growth potential, the market faces certain challenges. These include maintaining supply chain efficiency to ensure timely and reliable deliveries, managing logistics costs effectively, and competing with the established brick-and-mortar grocery stores. Furthermore, factors like concerns regarding food safety and the digital divide, particularly in less developed regions, need to be addressed to ensure sustainable and inclusive growth. The market is segmented by product type (household care, personal care, packaged food, etc.) and consumer demographics, allowing companies to tailor their strategies to specific customer segments and maximize their market share. Geographical penetration is another significant factor, with North America and Asia Pacific currently dominating the market, but substantial growth opportunities exist in other regions like Europe and Africa. The competitive landscape is intense, with large corporations vying for dominance while smaller niche players cater to specialized consumer demands.

The North America online grocery delivery market is experiencing robust growth, fueled by evolving consumer preferences and technological advancements. The market, currently valued at approximately $100 billion in 2025 (this is an estimation based on typical market sizes for similar sectors and the provided CAGR), is projected to achieve a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033. This expansion is driven by several factors, including the increasing convenience of online shopping, rising disposable incomes, and the growing adoption of smartphones and other digital devices. Consumers are increasingly drawn to the time-saving benefits and wider selection offered by online grocery platforms, particularly those who lead busy lifestyles or have limited mobility. Furthermore, the ongoing investments in last-mile delivery infrastructure and the development of advanced technologies such as automated warehouses and delivery robots are further propelling market growth. Competitive rivalry, however, is intensifying with major players like Amazon Fresh, Walmart, Instacart, and regional chains vying for market share through price competitiveness, superior delivery services, and enhanced customer loyalty programs. Despite the considerable growth potential, certain challenges persist. Concerns regarding food safety and freshness, the high cost of delivery, and the "digital divide" – where some demographics lack access to technology or reliable internet – remain hurdles to overcome. Strategies to address these challenges include improving delivery infrastructure, investing in robust quality control measures to ensure food freshness, offering various price points and delivery options, and implementing targeted initiatives to reach underserved populations. The market's future success hinges on the ability of companies to innovate, adapt to changing consumer preferences, and offer a seamless and reliable online grocery experience that overcomes existing limitations. The market is highly fragmented, with a mix of established grocery chains expanding their online presence and nimble tech-driven startups competing for dominance. Key drivers for this market are: Subscription Services are Driving Market Growth, Home Delivery is Expected to Drive New Growth Opportunities for the Market. Potential restraints include: Inefficient Delivery Management. Notable trends are: Growing Demand for Super Fast Delivery.

The Grocery Delivery App market has rapidly evolved into a vital component of the retail landscape, providing consumers with a convenient way to access groceries from the comfort of their homes. As lifestyles become busier and technology continues to advance, grocery delivery services have surged in popularity, effe

Shopping Application Market Outlook

The global shopping application market size is expected to reach an impressive figure of USD 375 billion by 2032, rising from USD 110 billion in 2023, exhibiting a Compound Annual Growth Rate (CAGR) of 14.2%. This phenomenal growth can be attributed to multiple factors, including the increasing penetration of smartphones, the rising number of internet users, and the growing preference for online shopping due to its convenience and time-saving attributes. The market is experiencing a surge as more consumers and businesses migrate to digital platforms for their shopping needs.

One of the significant growth factors propelling the shopping application market is the increasing adoption of smartphones and high-speed internet worldwide. As of 2023, the number of smartphone users has surpassed 6 billion, and this figure is expected to grow steadily. Enhanced mobile internet technology, such as 4G and the rollout of 5G, has facilitated seamless browsing and shopping experiences. Users can now shop anytime and anywhere, which has significantly amplified the usage of shopping applications. This technological advancement has not only improved user experience but also encouraged more vendors to develop sophisticated applications that cater to consumer needs.

Another crucial growth factor is the prevalent trend of digital transformation across industries. Retailers and wholesalers are increasingly investing in e-commerce platforms and mobile applications to reach a broader customer base. The COVID-19 pandemic has further accelerated this shift towards digital shopping. With lockdowns and social distancing norms in place, consumers were compelled to move online for their shopping needs. This change in consumer behavior has persisted even post-pandemic, with many preferring the convenience of online shopping over traditional methods. Consequently, shopping applications have gained a substantial foothold in the market.

Consumer behavior and preference shifts are also playing a pivotal role in the market's growth. There is an increasing demand for personalized shopping experiences, which shopping applications are uniquely positioned to provide. Features such as AI-driven recommendations, personalized discounts, and user-friendly interfaces have made shopping apps highly appealing. Furthermore, the integration of advanced technologies like augmented reality (AR) and virtual reality (VR) is enhancing the shopping experience by allowing users to virtually try products before making a purchase. These innovative features are attracting a large number of users, thereby driving market growth.

From a regional perspective, Asia Pacific stands out as the most lucrative market for shopping applications. The region's rapid economic growth, coupled with a massive population base and increasing internet penetration, makes it a hotbed for online retail activities. Countries such as China and India are at the forefront, with their burgeoning middle class and tech-savvy younger population driving the demand for shopping apps. North America and Europe are also significant markets, driven by high disposable incomes and advanced technological infrastructure. Meanwhile, regions like Latin America and the Middle East & Africa are gradually catching up, presenting untapped opportunities for market players.

Platform Analysis

The shopping application market can be segmented by platform into iOS, Android, and Web-based applications. The Android platform holds a dominant share due to its widespread use, especially in developing countries. With over 70% of the global smartphone market share, Android provides a vast user base for shopping applications. The platform's openness and flexibility allow developers to create and customize applications to meet specific consumer needs. Moreover, the lower cost of Android devices makes them accessible to a broader population, thereby increasing the reach of shopping applications.

iOS, on the other hand, caters to a more premium segment of the market. Although its market share is smaller compared to Android, iOS users tend to have higher purchasing power. This makes iOS a lucrative platform for developers targeting affluent consumers who are more likely to engage in online shopping. The platform is known for its robust security features and seamless user experience, which further enhances consumer trust and satisfaction. As a result, many high-end retailers and brands prefer to launch their shopping applications on iOS.

Web-based applications serve as an essential platform, e

The online fresh groceries market is experiencing robust growth, driven by increasing consumer preference for convenience, a wider selection of products, and technological advancements enabling efficient delivery and order fulfillment. The market's value in 2025 is estimated at $250 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This signifies a substantial expansion, projecting a market value exceeding $800 billion by 2033. Key drivers include the rising adoption of e-commerce, particularly among younger demographics, coupled with the increasing penetration of smartphones and reliable internet access. Furthermore, innovative business models like subscription services, personalized recommendations, and advanced logistics solutions are fueling this growth. The market is segmented by various factors, including product type (fruits, vegetables, dairy, etc.), delivery model (home delivery, click-and-collect), and consumer demographics. Major players like Amazon, Walmart, and Alibaba are investing heavily in infrastructure and technology to enhance their market share. However, challenges remain. Maintaining the quality and freshness of produce during transit and storage is a crucial aspect that requires continuous improvement in cold-chain logistics. Consumer concerns regarding food safety and product spoilage can act as restraints. Competition is fierce, necessitating strategic pricing, effective marketing, and a strong customer service approach for success. Expansion into underserved markets and catering to diverse consumer preferences are also essential for sustained growth within the online fresh groceries sector. The strategic focus on personalization, sustainable practices, and seamless integration of technology will define the success of companies in this dynamic market.

The online grocery market is experiencing robust growth, driven by the increasing adoption of e-commerce, rising disposable incomes, and a preference for convenient shopping experiences. The market, estimated at $500 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching a significant market valuation. This expansion is fueled by several key trends including the rise of quick commerce (deliveries within hours), the integration of advanced technologies like AI-powered recommendations and automated warehouses, and the increasing popularity of subscription services offering regular grocery deliveries. The segment's growth is further bolstered by the expansion of online grocery services into new geographic regions and demographics. Different delivery models, like home delivery and click-and-collect, cater to diverse consumer preferences, while the market caters to both personal and merchant needs. However, challenges remain. Significant investment in infrastructure and logistics is required to manage efficient delivery networks, particularly for perishable goods. Competition is intense, with established grocery chains and emerging tech-driven companies vying for market share. Furthermore, factors like concerns about food quality and freshness, the lack of in-person shopping experience, and technological hurdles in certain regions can restrain market growth in some areas. Nevertheless, the long-term outlook for online grocery remains positive, with substantial opportunities for growth and innovation. The industry's continued evolution is likely to be shaped by further technological advancements, improved supply chain management, and a deeper understanding of evolving consumer preferences. The major players mentioned, including Amazon, Ocado, Tesco, and Walmart, along with regional giants, are actively shaping this dynamic landscape through strategic acquisitions, technological innovations, and expansion efforts.

The European online grocery delivery market is experiencing robust growth, driven by increasing consumer demand for convenience, busy lifestyles, and the expanding reach of e-commerce. The market, valued at approximately €XX million in 2025 (estimated based on provided CAGR and market size), is projected to exhibit a Compound Annual Growth Rate (CAGR) of 17.20% from 2025 to 2033. This growth is fueled by several key factors. The rise of quick commerce (Q-commerce) platforms offering ultra-fast delivery within minutes is significantly impacting market dynamics, alongside the continued popularity of retail delivery services and meal kit subscriptions. Technological advancements, improved logistics infrastructure, and strategic partnerships between grocery retailers and delivery companies are further bolstering market expansion. While the market faces challenges such as maintaining profitability amidst intense competition and managing fluctuating food prices, the long-term outlook remains positive. Market segmentation reveals a strong presence of various delivery models. Retail delivery, offering a broader selection of groceries from established supermarkets, maintains a substantial market share. Quick commerce, with its focus on speed and convenience, is witnessing explosive growth, attracting a younger demographic. Meal kit delivery services cater to a niche market prioritizing convenience and pre-portioned ingredients. Key players like Flink, Just Eat, Delivery Hero, Uber Eats, Gorillas, Doordash, Amazon, Zomato, Rewe, and Getir are fiercely competing to capture market share through innovative offerings, aggressive marketing strategies, and expansion into new regions. The UK, Germany, France, and other major European countries are witnessing significant adoption of online grocery delivery, shaping regional variations in market penetration and growth patterns. Future growth will likely be influenced by factors such as technological innovation (e.g., automated warehouses, drone delivery), evolving consumer preferences, and the ability of companies to adapt to changing market conditions. Recent developments include: January 2023: Sainsbury's, the UK's largest supermarket chain, has partnered with online meal ordering and delivery service Eat Takeaway to provide speedier home delivery for groceries across the country. Customers can order things from Sainsbury's for delivery in under 30 minutes using the Just Eat app. The cooperation will begin with more than 175 stores by the end of February, with a nationwide rollout planned for 2023., February 2022: Rohlik Group, a Czech online grocery delivery company, has extended its German business, Knusper.de, by opening in Germany's Rhine-Main Metropolitan Region. Customers in and around Frankfurt, Mainz, Wiesbaden, and Darmstadt have begun to place orders with the company. Customers in these places can buy more than 9,000 different items. About 30% of these items come from local butchers, bakeries, and wineries.. Key drivers for this market are: Shift in Shopping Mode Preferences of the people, Increasing Urbanization; Attractive Offers and Payment Flexibility to Boost the Demand of the Market. Potential restraints include: Shift in Shopping Mode Preferences of the people, Increasing Urbanization; Attractive Offers and Payment Flexibility to Boost the Demand of the Market. Notable trends are: Attractive Offers and Payment Flexibility to Boost the Demand of the Market.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.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

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.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

Amongst the most used shopping apps, the source had identified **** different types related to grocery shopping in France in 2018. French consumers were asked if they used applications while shopping for groceries, where ** percent of respondents preferred using the generalist retail apps of big supermarket chains. Secondly, nutrition apps were popular amongst consumers, giving them more information about the nutritional values of certain products.