Dataset
africa-mauritius-hotel-room-occupancy-rate-2019-to-2023-for-large-hotels-by-321067f3
An Electric Sheep Africa compilation drawn from MDPA provides 10 rows tracking hotel room occupancy rates for large hotels in Mauritius across 2019 to 2023, distributed as Parquet with Hugging Face metadata and source lineage.
Data preview
Dataset card
This dataset from Electric Sheep Africa contains 10 rows of large-hotel room occupancy rates in Mauritius, drawn from MDPA and covering semesters between 2019 and 2023. It is distributed as a CC BY 4.0 licensed Parquet file on Hugging Face Datasets.
What this dataset contains
The release is a small tabular extract focusing on a single indicator, hotel-room occupancy for large hotels in Mauritius, disaggregated by semester. The source is MDPA, with provenance captured in the source_provider, source_dataset, and source_url columns. Coverage spans source_period_start_year through source_period_end_year, both set to 2019 and 2023 respectively in the schema.
Structure and fields
Each row is keyed by indicator_id, country_iso3 ("MU"), and year, with the numeric observation stored in the float64 value column and its unit recorded in the unit field. A dimension_year column distinguishes "Semester 1" and "Semester 2" observations, while indicator_name, country_name, source_period_label, source_resource, source_resource_id, and source_package_id carry descriptive metadata. A retrieved_at timestamp and license_id field document when and under what terms the data was harvested.
How analysts use it
Analysts can pivot the value column by year and dimension_year to build a semester-level occupancy series for Mauritius, or join it with other MDPA-derived series using indicator_id and country_iso3. The dataset is tagged for tabular regression and time-series forecasting, supporting lightweight backtesting or nowcasting exercises. Given the very small row count, it is best treated as a reference benchmark rather than a primary signal for sector or country-level investment decisions.
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