| ---
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| pretty_name: SpatialEpiBench TsFile
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| license: cc-by-4.0
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| tags:
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| - tabular
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| - timeseries
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| - time-series
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| - geospatial
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| - epidemiology
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| - spatial-epidemiology
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| - epidemic-forecasting
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| - public-health
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| - benchmark
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| - graph
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| - spatiotemporal
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| - tsfile
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| task_categories:
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| - time-series-forecasting
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| viewer: false
|
| ---
|
|
|
| # SpatialEpiBench TsFile
|
|
|
| This repository contains a TsFile conversion of the Hugging Face dataset
|
| [`ruiqil/SpatialEpiBench`](https://huggingface.co/datasets/ruiqil/SpatialEpiBench).
|
|
|
| The description below separates information taken from the original dataset
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| card from the changes made during this TsFile conversion.
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|
|
| ## ⚠️ About the Dataset Viewer
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|
|
| **The Hugging Face dataset viewer is disabled for this repository on purpose.**
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|
|
| The actual data lives in **11 `.tsfile` files** (Apache IoTDB TsFile, a binary
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| time-series format). The HF viewer does **not** support `.tsfile`, so it cannot
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| render the real data. The only files the viewer *could* auto-preview are the
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| sidecar `.csv` files (`adjacency/*_adj.csv`, `column_mapping.csv`,
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| `conversion_summary.csv`) — but those are **static graph/metadata tables, not
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| the time-series data**. Showing them would misrepresent this dataset as having
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| no timestamps, so the viewer is turned off (`viewer: false`).
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|
|
| Each `.tsfile` does contain a proper time axis. Below is a real preview read
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| back from `AUcase/AUcase.tsfile` (millisecond `Time` plus regional case counts;
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| only 4 of 8 regions shown for width):
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|
|
| | Time (epoch ms) | Time (UTC) | australian_capital_territory | new_south_wales | queensland | victoria |
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| |---:|---|---:|---:|---:|---:|
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| | 1585699200000 | 2020-04-01 | 4 | 150 | 38 | 51 |
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| | 1585785600000 | 2020-04-02 | 3 | 116 | 54 | 68 |
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| | 1585872000000 | 2020-04-03 | 4 | 91 | 38 | 49 |
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| | 1585958400000 | 2020-04-04 | 2 | 104 | 27 | 30 |
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| | 1586044800000 | 2020-04-05 | 3 | 87 | 7 | 20 |
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|
|
| To read a `.tsfile`, use the TsFile SDK (Python or Java):
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|
|
| ```python
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| from tsfile import TsFileReader, ColumnCategory
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|
|
| reader = TsFileReader("AUcase/AUcase.tsfile")
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| schemas = reader.get_all_table_schemas()
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| table = next(iter(schemas)) # e.g. "aucase"
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| fields = [c.get_column_name() for c in schemas[table].get_columns()
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| if c.get_category() in (ColumnCategory.FIELD, ColumnCategory.TAG)]
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| with reader.query_table(table, fields, batch_size=65536) as rs:
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| batch = rs.read_arrow_batch() # Arrow batch; includes the `time` column
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| print(batch.to_pandas().head())
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| ```
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|
|
| ## Original Dataset Information
|
|
|
| Original dataset: <https://huggingface.co/datasets/ruiqil/SpatialEpiBench>
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|
|
| According to the original dataset README, SpatialEpiBench is a benchmark
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| collection of 11 spatiotemporal epidemic forecasting datasets. It covers
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| public-health surveillance modalities including influenza-like illness
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| surveillance rates, confirmed cases, test positivity, inpatient and
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| outpatient hospitalizations, hospital admissions, doctor visits, and deaths.
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| The datasets span the United States, Canada, and Australia, with daily or
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| weekly temporal resolution depending on the data source.
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|
|
| The original repository provides each time-series dataset as a CSV file,
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| paired with a corresponding spatial adjacency matrix in a `_adj.csv` file.
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| The original dataset card declares license `CC-BY-4.0`.
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|
|
| Original dataset overview from the source README:
|
|
|
| | Dataset | Frequency | Country | Modality | Time |
|
| |---|---|---|---|---|
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| | `AUcase` | daily | Australia | cases | 2020-2021 |
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| | `CAcase` | daily | Canada | cases | 2020-2021 |
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| | `CANpositivity` | daily | U.S. | test positivity | 2020-2021 |
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| | `CHNGinpatient` | daily | U.S. | inpatient hospitalizations | 2020-2024 |
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| | `CHNGoutpatient` | daily | U.S. | outpatient visits | 2020-2024 |
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| | `CPRadmissions` | daily | U.S. | hospital admissions | 2020-2023 |
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| | `DVcli` | daily | U.S. | doctor visits | 2020-present |
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| | `HHShosp` | daily | U.S. | hospitalizations | 2021-2024 |
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| | `ILI2019` | weekly | U.S. | surveillance rate | 2010-present |
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| | `JHUcase` | daily | U.S. | cases | 2020-2023 |
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| | `NCHSdeaths` | weekly | U.S. | deaths | 2020-present |
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|
|
| ## Converted Files
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|
|
| Each original time-series CSV was converted into one TsFile. The conversion
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| keeps the original wide-table layout as closely as possible: each regional
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| CSV column remains a measurement column in the matching TsFile.
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|
|
| | Source CSV | TsFile | Source rows | Regions | Source time column | First time | Last time | Missing source values | Renamed region columns |
|
| |---|---|---:|---:|---|---|---|---:|---:|
|
| | `AUcase.csv` | `AUcase/AUcase.tsfile` | 640 | 8 | `time_value` | 2020-04-01 | 2021-12-31 | 0 | 5 |
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| | `CAcase.csv` | `CAcase/CAcase.tsfile` | 640 | 13 | `time_value` | 2020-04-01 | 2021-12-31 | 0 | 6 |
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| | `CANpositivity.csv` | `CANpositivity/CANpositivity.tsfile` | 642 | 51 | `time_value` | 2020-03-01 | 2021-12-02 | 318 | 0 |
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| | `CHNGinpatient.csv` | `CHNGinpatient/CHNGinpatient.tsfile` | 1309 | 51 | `time_value` | 2020-01-01 | 2023-08-01 | 34 | 0 |
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| | `CHNGoutpatient.csv` | `CHNGoutpatient/CHNGoutpatient.tsfile` | 1309 | 51 | `time_value` | 2020-01-01 | 2023-08-01 | 0 | 0 |
|
| | `CPRadmissions.csv` | `CPRadmissions/CPRadmissions.tsfile` | 644 | 51 | `time_value` | 2020-12-16 | 2023-02-21 | 0 | 0 |
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| | `DVcli.csv` | `DVcli/DVcli.tsfile` | 2162 | 51 | `time_value` | 2020-02-01 | 2026-01-01 | 0 | 0 |
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| | `HHShosp.csv` | `HHShosp/HHShosp.tsfile` | 978 | 51 | `time_value` | 2021-08-23 | 2024-04-26 | 0 | 0 |
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| | `ILI2019.csv` | `ILI2019/ILI2019.tsfile` | 481 | 52 | `time` | 2010-10-03 | 2019-12-29 | 0 | 0 |
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| | `JHUcase.csv` | `JHUcase/JHUcase.tsfile` | 730 | 51 | `time_value` | 2020-04-01 | 2022-03-31 | 0 | 0 |
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| | `NCHSdeaths.csv` | `NCHSdeaths/NCHSdeaths.tsfile` | 310 | 51 | `time_value` | 2020-01-26 | 2025-12-28 | 991 | 0 |
|
|
|
| ## Conversion Changes
|
|
|
| Compared with the original CSV layout, this conversion made these changes:
|
|
|
| - One source time-series CSV was converted to one TsFile, resulting in 11
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| TsFile files.
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| - The original date/week column (`time_value`, or `time` for `ILI2019`) was
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| parsed into the TsFile `Time` column at millisecond precision.
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| - Regional measurement columns were kept as wide measurement columns.
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| - Region column names containing spaces or other schema-unsafe characters
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| were normalized for TsFile/schema parsing, for example spaces were
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| replaced with underscores. The full mapping is provided in
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| `column_mapping.csv`.
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| - Missing numeric values from the source CSV files remain null/missing in
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| the staged Parquet input. The TsFile import represents these as absent
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| values for the affected measurement/time.
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| - The `_adj.csv` spatial adjacency matrices were not written into TsFile
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| because they are static graph metadata, not time-varying measurements.
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| They are preserved unchanged under `adjacency/`.
|
| - The original dataset README is included as `SOURCE_DATASET_README.md` for
|
| reference.
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|
|
| No image, video, audio, or other media files were present in the source
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| repository. The source files are CSV tables.
|
|
|
| ## Sidecar Files
|
|
|
| - `adjacency/*.csv`: original spatial adjacency matrices copied from the
|
| source dataset.
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| - `column_mapping.csv`: original region column names and their TsFile-safe
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| measurement names.
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| - `conversion_summary.csv`: row/region/missing-value summary computed from
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| the source CSV files used for this conversion.
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| - `SOURCE_DATASET_README.md`: original Hugging Face dataset README downloaded
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| from the source repository.
|
|
|
| ## Validation Summary
|
|
|
| - Source value cells across all main CSV files: 450736
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| - Missing source value cells: 1343
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| - Region columns renamed for TsFile compatibility: 11
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| - All 11 generated `.tsfile` files were validated locally as present and
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| non-empty.
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|
|
