Time series data for the statistic Rural population, female (% of total) and country Germany. Indicator Definition:Female rural population is the percentage of females who live in rural areas to total population.The Serie's long term average value is 13.22. It's latest available value, on 12/31/2015, is 8.80 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2015, to it's latest available value, on 12/31/2015, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1985, to it's latest available value, on 12/31/2015, is -13.39%.

This dataset was collected from a large survey and several public datasets focusing on rural areas in Bavaria, Germany. The research was conducted in Bavaria due to its advanced technology infrastructure, diverse economy, and ongoing rural e-commerce development initiatives. The data sources include government statistics, rural development projects, and local e-commerce and infrastructural organizations. The dataset is designed to assess and analyze key variables influencing rural e-commerce growth.

The dataset spans five years of trends and indicators, providing a rich foundation for machine learning studies. Its multidimensional nature allows for the application of sophisticated models to uncover the primary drivers of e-commerce success in rural areas. The dataset has been validated using multiple independent sources to ensure its reliability and accuracy.

Features: The dataset includes the following features:

Economic Factors:

Household_Income: Annual household income in the rural region. Employment_Rate: Proportion of the rural population employed. Agricultural_Productivity: Measure of agricultural output in the region. Tech_Expenditure: Spending on technology-related products and services. Technological Factors:

Internet_Penetration: Percentage of the population with access to the internet. Smartphone_Usage: Percentage of the population owning and using smartphones. Ecommerce_Awareness: Awareness of e-commerce platforms within the rural community. Infrastructure Factors:

Road_Connectivity: Quality and availability of road infrastructure. Warehouse_Proximity: Average distance to major warehouses. Electricity_Availability: Hours of electricity availability per day. Logistics_Performance: Efficiency of logistics and supply chains. Social and Cultural Factors:

Literacy_Rate: Proportion of the population that is literate. Gender_Equality_Index: Index representing gender parity in the region. Trust_in_Online_Transactions: Level of trust in online transactions and e-commerce. E-Commerce Adoption Metrics:

Ecommerce_Growth: Growth rate of e-commerce activity in the region. Average_Order_Value: Average order value of e-commerce transactions. Repeat_Customer_Rate: Percentage of repeat customers for e-commerce platforms. Policy and Support Indicators:

Subsidy_Accessibility: Availability of government subsidies to support e-commerce. Skill_Program_Availability: Availability of training programs related to e-commerce. Labels: Priority_Score: A regression label calculated from various factors including household income, internet penetration, and logistics performance. Priority_Level: A categorical label indicating the priority level for e-commerce development in the region, categorized into Low, Medium, and High. This dataset is suitable for applications in e-commerce prediction, rural development studies, and machine learning models targeting the optimization of e-commerce readiness in rural areas.