Indicators of urban competitive vulnerability in Spanish tourist cities (2014–2024)
Description
This dataset provides a harmonised panel of economic, social and tourism performance indicators for six major tourist cities in Spain—Barcelona, Madrid, Palma de Mallorca, Seville, Málaga and Valencia—covering the period from 2014 to 2024. The data have been compiled from official statistical sources and systematically organised to support empirical analyses of urban dynamics, competitiveness and vulnerability in tourism-oriented cities. The variables capture key dimensions of urban performance, including economic activity, labour market conditions, social and demographic characteristics, tourism intensity and accommodation capacity. Together, these indicators allow for a multidimensional assessment of how tourism development interacts with broader socio-economic structures at the urban level over time. The dataset is designed to facilitate comparative and longitudinal analyses, enabling researchers to explore trends, disparities and structural changes across cities and periods. It is particularly suitable for studies on urban competitiveness, vulnerability, resilience and sustainable tourism development, as well as for quantitative modelling and policy-oriented research. The data underpin the empirical analysis presented in the associated research article titled “Urban competitive vulnerability in tourist cities: An integrated framework and empirical evidence from Spain”.
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Steps to reproduce
The dataset was constructed through the systematic collection and integration of secondary data from official and publicly available statistical sources at the urban level in Spain. Data were gathered for the period 2014–2024 for six major tourist cities: Barcelona, Madrid, Palma de Mallorca, Seville, Málaga and Valencia. Economic, social and tourism-related indicators were extracted from national and regional statistical offices and tourism observatories, including labour market statistics, demographic indicators, tourism demand and accommodation capacity measures. Only harmonised and regularly reported indicators were selected in order to ensure temporal consistency and cross-city comparability. All variables were standardised to a common annual frequency and spatial scale. Data cleaning procedures included the verification of units of measurement, the treatment of missing values, and consistency checks across sources and time. When necessary, minor adjustments were applied to ensure methodological coherence over the full study period. The dataset was compiled and processed using statistical software, enabling reproducible data management and preparation workflows. The resulting panel dataset allows for longitudinal and comparative analyses of urban economic, social and tourism performance and serves as the empirical basis for the analyses presented in the associated research article.
Institutions
- Universidad de La Laguna