DemAI Project Dataset: Multisource Socioeconomic, Demographic, Regional, Rural, Health, and Geospatial Indicators for AI-Driven Territorial Analysis

Description

This dataset was prepared as part of the DemAI project, which aims to support AI-driven territorial, socioeconomic, demographic, rural, health, and geospatial analysis. The dataset brings together multiple structured CSV files covering regional and municipal indicators relevant to population change, territorial development, rural vulnerability, health access, socioeconomic conditions, infrastructure, and international development comparisons. The dataset is organized into thematic groups, including Spanish and INE-based indicators, Eurostat regional indicators, international development indicators, and rural-health-geospatial indicators. These files are intended to support research, data exploration, machine learning experimentation, dashboard development, and policy-oriented analysis related to demographic change, rural development, regional inequality, and public-service accessibility. The dataset package includes CSV data files, a file manifest, a data dictionary, checksum information, metadata documentation, and a README file describing the structure and intended use of the files. The data can be used for preprocessing, exploratory data analysis, feature engineering, visualization, and the development of AI models for territorial intelligence and decision-support systems. Users should review the accompanying metadata and file manifest before analysis. Where applicable, source information and notes are provided to clarify the origin, structure, and intended use of each file.

Steps to reproduce

Step 1: Download all files and folders from this Mendeley Data record. Step 2: Keep the original folder structure unchanged. The dataset is organized into three main folders: data, documentation, and metadata. Step 3: Open the README.md file first. This file explains the purpose of the DemAI dataset, the folder organization, and the intended use of the data. Step 4: Open the metadata folder and review the file_manifest.csv file. This file lists the dataset files, their categories, file names, row and column information, and checksum information. Step 5: Open the metadata folder and review the data_dictionary.csv file. This file describes the variables and column structure used in the dataset files. Step 6: Open the data folder to access the CSV dataset files. These files can be loaded using Excel, Python, R, Power BI, Tableau, or any standard data analysis tool. Step 7: For Python users, load any CSV file using pandas, for example: pandas.read_csv("data/filename.csv"). Step 8: Use the documentation folder for additional notes about the dataset structure, sources, validation, rights, and publication information. Step 9: Perform the required analysis, such as data cleaning, exploratory data analysis, visualization, feature engineering, machine learning, or dashboard development. Step 10: When using this dataset in research, reports, publications, or software tools, cite this Mendeley Data record using the citation information provided in CITATION.cff.

Institutions

• Universidad de Salamanca

  Castille and León, Salamanca

Categories

Licence