Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains the predicted prices of the asset The most delicious food over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Dataset

This dataset was created by Khalid Ashik

Released under Apache 2.0

Contents

This dataset contains the predicted prices of the asset Food over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

The Consumer Price Index (CPI) for food is a component of the all-items CPI. The CPI measures the average change over time in the prices paid by urban consumers for a representative market basket of consumer goods and services. While the all-items CPI measures the price changes for all consumer goods and services, including food, the CPI for food measures the changes in the retail prices of food items only.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

This dataset contains the predicted prices of the asset Lets Get Some Food over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

The overall food industry in China is forecast to increase to a volume of *** billion kilograms by 2030. Similar to today, Bread & Cereal Products is estimated to remain the biggest segment in the market with ****** billion kilograms.

Food Price Index in World decreased to 126.40 Index Points in October from 128.50 Index Points in September of 2025. This dataset includes a chart with historical data for World Food Price Index.

Food Price Risk Indicators via Satellite Market Outlook

According to our latest research, the global Food Price Risk Indicators via Satellite market size reached USD 2.47 billion in 2024, with a robust year-over-year growth propelled by advancements in remote sensing and data analytics technologies. The market is projected to grow at a CAGR of 11.2% from 2025 to 2033, culminating in a forecasted market size of USD 6.45 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for real-time, reliable, and scalable food price risk assessment tools in the face of mounting global food security concerns and volatile agricultural commodity markets.

The surge in adoption of satellite-based food price risk indicators is largely fueled by the growing frequency and severity of climate-induced disruptions in agricultural production. As global weather patterns become increasingly erratic, stakeholders across the food value chain are seeking advanced monitoring and forecasting tools to mitigate risks associated with price volatility. The integration of high-resolution remote sensing data with sophisticated data analytics platforms enables more accurate and timely assessments of crop health, yield forecasts, and supply chain vulnerabilities. This, in turn, empowers governments, agribusinesses, and commodity traders to make informed decisions, optimize resource allocation, and implement proactive risk management strategies, thereby supporting overall market expansion.

Another significant growth driver is the rising emphasis on food security and disaster preparedness among both developed and emerging economies. International organizations, NGOs, and government agencies are increasingly leveraging satellite-derived data to assess food availability, monitor droughts, and predict potential famine conditions. This trend is further bolstered by the proliferation of public-private partnerships aimed at enhancing agricultural resilience through technology-driven solutions. The increasing affordability and accessibility of satellite imagery, coupled with advancements in artificial intelligence and machine learning, are making these tools more accessible to a wider range of end-users, further accelerating market adoption and growth.

Furthermore, the expanding role of digital transformation in agribusiness is reshaping the competitive landscape of the Food Price Risk Indicators via Satellite market. Companies are investing heavily in the development of integrated platforms that combine remote sensing, predictive analytics, and visualization tools to deliver comprehensive risk assessment solutions. These innovations are not only improving the accuracy and granularity of food price forecasts but are also enabling seamless integration with existing enterprise systems. As a result, stakeholders across the agricultural ecosystem are increasingly reliant on satellite-based indicators to enhance operational efficiency, reduce exposure to price shocks, and support sustainable food system development.

Regionally, North America and Europe are leading the adoption of satellite-based food price risk indicators, driven by advanced technological infrastructure, strong regulatory frameworks, and high levels of investment in agricultural innovation. However, the Asia Pacific region is emerging as a key growth engine, supported by rapid digitalization, expanding agritech ecosystems, and growing awareness of food security challenges. Latin America and the Middle East & Africa are also witnessing increased uptake, particularly in countries with significant agricultural output and vulnerability to climate risks. The global market is thus characterized by a dynamic interplay of regional drivers, technological advancements, and evolving stakeholder needs, setting the stage for sustained growth over the forecast period.

Solution Type Analysis

The Solution Type segment of the Food Price Risk Indicators via Satellite market encompasses a wide range of offerings, including Remote Sensing, Data Analytics, Forecasting Tools, Visualization Platforms, and other specialized solutions. Remote Sensing remains the cornerstone of this segment, leveraging high-resolution satellite imagery to monitor crop conditions, soil moisture, and land use patterns in real time. The proliferation of both optical and radar satellites has significantly enhanced the granularity and frequency of data collection, enabling more accurate and tim

This dataset contains the predicted prices of the asset TE-FOOD over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Cost of food in India decreased 5.02 percent in October of 2025 over the same month in the previous year. This dataset provides - India Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Food Price Risk Indicators via Satellite Market Outlook

According to our latest research, the global Food Price Risk Indicators via Satellite market size reached USD 1.82 billion in 2024, registering a robust year-on-year growth. The market is expected to expand at a CAGR of 12.4% from 2025 to 2033, reaching a projected value of USD 5.24 billion by 2033. This growth is primarily driven by the increasing need for accurate, real-time agricultural data to mitigate food price volatility and ensure global food security, as per the latest research findings.

The primary growth factor for the Food Price Risk Indicators via Satellite market is the rising global concern over food security, especially in the context of climate change, geopolitical tensions, and supply chain disruptions. Governments, international organizations, and private enterprises are recognizing the critical importance of reliable, timely, and actionable data on crop health, yield forecasts, and market trends. Satellite-based solutions provide a unique advantage by delivering comprehensive, near-real-time insights into agricultural production and distribution networks, enabling stakeholders to anticipate and respond to food price fluctuations more effectively. The integration of advanced remote sensing technologies and sophisticated data analytics tools further enhances the precision of these indicators, supporting better decision-making across the food value chain.

Another significant driver is the rapid advancement in satellite imaging and geospatial analytics technologies. The proliferation of high-resolution optical and radar satellites, coupled with machine learning-powered data analytics platforms, has revolutionized the ability to monitor agricultural landscapes at scale. These advancements are making it possible to detect subtle changes in crop conditions, soil moisture, and weather patterns, all of which are critical variables in forecasting food prices. As the cost of satellite data acquisition continues to decrease, accessibility to these technologies is broadening, allowing even small and medium-sized enterprises and NGOs to leverage satellite-derived insights for risk management and strategic planning.

Additionally, the increasing adoption of satellite-based food price risk indicators by financial institutions, agribusinesses, and supply chain operators is fueling market expansion. These stakeholders are leveraging satellite-derived data to inform commodity trading, insurance underwriting, and supply chain logistics, thereby reducing exposure to price shocks and market uncertainties. The integration of visualization tools and customizable dashboards is also enhancing the user experience, enabling non-technical users to interpret complex datasets with ease. As digital transformation accelerates across the agricultural sector, the demand for scalable, interoperable solutions that can seamlessly integrate with existing enterprise systems is expected to drive further innovation and market growth.

From a regional perspective, North America and Europe currently lead the Food Price Risk Indicators via Satellite market due to their advanced technological infrastructure, significant investments in agricultural innovation, and strong regulatory frameworks. However, the Asia Pacific region is poised for the fastest growth, driven by its large agricultural base, increasing government initiatives to enhance food security, and rising adoption of digital agriculture solutions. Latin America and the Middle East & Africa are also witnessing steady growth, supported by international development programs and growing awareness of the benefits of satellite-based agricultural monitoring. The global market landscape is thus characterized by both mature and emerging markets, each with unique opportunities and challenges.

Solution Type Analysis

The Solution Type segment of the Food Price Risk Indicators via Satellite market encompasses remote sensing, data analytics, visualization tools, and other special

Over the last two observations, the price per unit is forecast to significantly increase in all segments. This reflects the overall trend throughout the entire forecast period from 2020 to 2030. It is estimated that the price per unit is continuously rising in all segments. In this regard, the baby food segment achieves the highest value of ***** U.S. dollars in 2030.

This dataset contains the predicted prices of the asset FOOD FOR GAZA over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Cost of food in Ireland increased 4.50 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Ireland Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The price per unit in the food market worldwide was modeled to be **** U.S. dollars in 2024. Following a continuous upward trend, the price per unit has risen by **** U.S. dollars since 2018. Between 2024 and 2030, the price per unit will rise by **** U.S. dollars, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Food.

This dataset provides an analysis of average monthly prices for four essential food items, namely Eggs, Milk, Bread, and Potatoes, in five different countries: Australia, Japan, Canada, South Africa, and Sweden. The dataset spans a five-year period, from 2018 to 2022, offering a comprehensive overview of how food prices have evolved over time in these nations.

The dataset includes information on the average monthly prices of each food item in the respective countries. This information can be valuable for studying and comparing the cost of living, assessing economic trends, and understanding variations in food price dynamics across different regions.

Use Cases:

Comparative Analysis: Researchers and analysts can compare food prices across the five countries over the five-year period to identify patterns, trends, and variations. This analysis can help understand differences in purchasing power and economic factors impacting food costs.

Cost of Living Studies: The dataset can be used to examine the cost of living in different countries, specifically focusing on the expenses related to basic food items. This information can be beneficial for individuals considering relocation or policymakers aiming to evaluate living standards.

Economic Studies: Economists and policymakers can utilize this dataset to analyze the impact of economic factors, such as inflation or currency fluctuations, on food prices in different countries. It can provide insights into the stability and volatility of food markets in each region.

Forecasting and Planning: Businesses in the food industry can leverage the dataset to forecast future food price trends and plan their operations accordingly. The historical data can serve as a foundation for predictive models and assist in optimizing pricing strategies and supply chain management.

Note: The dataset is based on average monthly prices and does not capture individual variations or specific regions within each country. Further analysis and interpretation should consider additional factors like seasonal influences, local market dynamics, and consumer preferences.

How much do fruits and vegetables cost? The USDA, Economic Research Service (ERS) estimated average prices for more than 150 commonly consumed fresh and processed fruits and vegetables. Reported estimates include each product's average retail price and price per edible cup equivalent (i.e., the unit of measurement for Federal recommendations for fruit and vegetable consumption). Average retail prices are reported per pound or per pint. For many fruits and vegetables, a 1-cup equivalent equals the weight of enough edible food to fill a measuring cup. USDA, ERS calculated average prices at retail stores using 2013, 2016, 2020, and 2022 retail scanner data from Circana (formerly Information Resources Inc. (IRI)). A selection of retail establishments—grocery stores, supermarkets, supercenters, convenience stores, drug stores, and liquor stores—across the United States provides Circana with weekly retail sales data (revenue and quantity).

USDA, ERS reports average prices per edible cup equivalent to inform policymakers and nutritionists about how much money it costs U.S. households to eat a sufficient quantity and variety of fruits and vegetables. Every 5 years the Departments of Agriculture and Health and Human Services release a new version of the Dietary Guidelines for Americans with information about how individuals can achieve a healthy diet. However, the average consumer falls short in meeting these recommendations. Many people consume too many calories from refined grains, solid fats, and added sugars, and do not eat enough whole grains, fruits, and vegetables. Are food prices a barrier to eating a healthy diet? USDA, ERS research using this dataset examines the quantity and variety of fruits and vegetables that a household can afford with a limited budget. See:

• The Cost of Satisfying Fruit and Vegetable Recommendations in the Dietary Guidelines
• For Supplemental Nutrition Assistance Program (SNAP) Households, Fruit and Vegetable Affordability Is Partly a Question of Budgeting
• Americans Still Can Meet Fruit and Vegetable Dietary Guidelines for $2.10-$2.60 per Day

USDA, ERS fruit and vegetable prices will be updated each year, subject to data availability. When generating estimates using 2013, 2016, 2020, and 2022 data, USDA, ERS researchers priced similar fruit and vegetable products. However, because of different methods for coding the underlying Circana data, the entry of new products into the market, the exit of old products from the market, and other factors, the data are not suitable for making year-to-year comparisons. These data should not be used for making inferences about price changes over time.

For data on retail food price trends, see the USDA, ERS’ Food Price Outlook (FPO). The FPO provides food price data and forecasts changes in the Consumer Price Index (CPI) and Producer Price Index (PPI) for food.

For additional data on food costs, see the USDA, ERS’ Purchase to Plate (PP-Suite). The PP-Suite reports a U.S. household’s costs to consume other categories of foods in addition to fruits and vegetables, such as meats, seafood, and cereal and bakery products. Food groupings in the PP-Suite are based on the USDA, Agricultural Research Service’s (ARS) Food and Nutrient Database for Dietary Studies (FNDDS). This allows users to import price estimates for foods found in USDA dietary survey data. USDA, ARS’ FNDDS food groupings are broader than the specific food products priced for constructing this data product. They also include both conventional and organic products. For example, the PP-Suite average price to consume broccoli purchased raw is the average price paid for organic and conventional heads, crowns, and florets. By contrast, this data product distinguishes and separately reports the average costs to consume conventional raw broccoli purchased as heads and florets.

• Fruit
• Vegetables

Dataset Overview

This dataset contains 2,000 rows of data from coffee shops, offering detailed insights into factors that influence daily revenue. It includes key operational and environmental variables that provide a comprehensive view of how business activities and external conditions affect sales performance. Designed for use in predictive analytics and business optimization, this dataset is a valuable resource for anyone looking to understand the relationship between customer behavior, operational decisions, and revenue generation in the food and beverage industry.

Columns & Variables

The dataset features a variety of columns that capture the operational details of coffee shops, including customer activity, store operations, and external factors such as marketing spend and location foot traffic.

1. Number of Customers Per Day

   • The total number of customers visiting the coffee shop on any given day.
   • Range: 50 - 500 customers.

2. Average Order Value ($)

   • The average dollar amount spent by each customer during their visit.
   • Range: $2.50 - $10.00.

3. Operating Hours Per Day

   • The total number of hours the coffee shop is open for business each day.
   • Range: 6 - 18 hours.

4. Number of Employees

   • The number of employees working on a given day. This can influence service speed, customer satisfaction, and ultimately, sales.
   • Range: 2 - 15 employees.

5. Marketing Spend Per Day ($)

   • The amount of money spent on marketing campaigns or promotions on any given day.
   • Range: $10 - $500 per day.

6. Location Foot Traffic (people/hour)

   • The number of people passing by the coffee shop per hour, a variable indicative of the shop's location and its potential to attract customers.
   • Range: 50 - 1000 people per hour.

Target Variable

• Daily Revenue ($)
  • This is the dependent variable representing the total revenue generated by the coffee shop each day.
  • It is calculated as a combination of customer visits, average spending, and other operational factors like marketing spend and staff availability.
  • Range: $200 - $10,000 per day.

Data Distribution & Insights

The dataset spans a wide variety of operational scenarios, from small neighborhood coffee shops with limited traffic to larger, high-traffic locations with extensive marketing budgets. This variety allows for exploring different predictive modeling strategies. Key insights that can be derived from the data include:

• The effect of marketing spend on daily revenue.
• The correlation between customer count and daily sales.
• The relationship between staffing levels and revenue generation.
• The influence of foot traffic and operating hours on customer behavior.

Use Cases & Applications

The dataset offers a wide range of applications, especially in predictive analytics, business optimization, and forecasting:

• Predictive Modeling: Use machine learning models such as regression, decision trees, or neural networks to predict daily revenue based on operational data.
• Business Strategy Development: Analyze how changes in marketing spend, staff numbers, or operating hours can optimize revenue and improve efficiency.
• Customer Insights: Identify patterns in customer behavior related to shop operations and external factors like foot traffic and marketing campaigns.
• Resource Allocation: Determine optimal staffing levels and marketing budgets based on predicted sales, improving overall profitability.

Real-World Applications in the Food & Beverage Industry

For coffee shop owners, managers, and analysts in the food and beverage industry, this dataset provides an essential tool for refining daily operations and boosting profitability. Insights gained from this data can help:

• Optimize Marketing Campaigns: Evaluate the effectiveness of daily or seasonal marketing campaigns on revenue.
• Staff Scheduling: Predict busy days and ensure that the right number of employees are scheduled to maximize efficiency.
• Revenue Forecasting: Provide accurate revenue projections that can assist with financial planning and decision-making.
• Operational Efficiency: Discover the most profitable operating hours and adjust business hours accordingly.

This dataset is also ideal for aspiring data scientists and machine learning practitioners looking to apply their skills to real-world business problems in the food and beverage sector.

Conclusion

The Coffee Shop Revenue Prediction Dataset is a versatile and comprehensive resource for understanding the dynamics of daily sales performance in coffee shops. With a focus on key operational factors, it is perfect for building predictive models, ...

Cost of food in South Africa increased 3.90 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - South Africa Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.