DOI: 10.5061/dryad.905qfttzb
title: A landslide dataset for Pingjiang County, Hunan Province

description: Landslides pose significant geological hazards in Pingjiang County, and
 understanding their susceptibility is crucial for effective disaster
 prevention and mitigation. However, detailed datasets capturing the
 multi-factorial drivers of landslide susceptibility in this region remain
 underdocumented. Here, we present a sample dataset of landslide
 susceptibility in Pingjiang County, comprising more than 400000 samples
 with multiple relevant variables. The dataset includes topographic factors
 (Elevation, Slope, Aspect, Plan curvature), proximity metrics (Distance to
 faults [Dis_fault], Distance to roads [Dis_road]), vegetation index (NDVI
 [Normalized Difference Vegetation Index]), lithological information
 (Lithology), and a potential soil/geomorphic index (SPI). This dataset is
 designed to facilitate analyses of how various environmental and
 topographic factors collectively influence landslide susceptibility. It
 can support investigations into the relative importance of individual
 variables—such as the role of slope steepness versus proximity to faults,
 or the interplay between vegetation cover (NDVI) and lithology—in driving
 landslide occurrence. Potential applications include statistical modeling
 to identify key susceptibility drivers, or comparative analyses of how
 factor combinations vary across samples. While specific analyses of the
 dataset are beyond the scope here, the inclusion of diverse variables
 enables exploration of complex relationships: for instance, whether areas
 with steeper slopes (higher Slope values) and closer proximity to roads
 (lower Dis_road) exhibit heightened susceptibility, or how lithological
 differences (Lithology) modulate the impact of topographic factors. This
 dataset provides a foundational resource for advancing landslide
 susceptibility research in Pingjiang County, aiding in the development of
 targeted hazard mitigation strategies and enhancing understanding of
 regional landslide mechanisms.
description: # A landslide dataset for Pingjiang County, Hunan Province Dataset DOI:
 [10.5061/dryad.905qfttzb](10.5061/dryad.905qfttzb) ## Description of the
 data and file structure This dataset contains more than 400000 samples of
 landslide susceptibility-related variables collected from Pingjiang
 County, designed to support analyses of environmental and geological
 factors influencing landslide occurrence. Each sample is uniquely
 identified by `OBJECTID` and includes 9 quantitative variables capturing
 topographic, geological, vegetation, and proximity characteristics. ###
 Files and variables #### Variables A comprehensive list of variables,
 including definitions, units, abbreviations, and notes on missing values,
 is provided below: | Variable Name | Abbreviation (if applicable) |
 Definition | Units of Measurement | Notes on Missing Values | |
 :------------ |
 :------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------- | :---------------------------------------------- | | OBJECTID | N/A | Unique identifier for each sample, used to distinguish individual records. | Unitless (integer values: 1–26) | No missing values; all samples have unique IDs. | | Elevation | N/A | Relative elevation of the sampling site, normalized to a relative scale. | Unitless (normalized; reflects relative height within the study area) | No missing values. | | Plan\_cur | Plan\_cur (abbreviation for "Plan Curvature") | Horizontal curvature of the terrain, describing how slope direction changes in a horizontal plane (concave = convergence of water; convex = divergence). | Unitless (normalized) | No missing values. | | Slope | N/A | Steepness of the terrain, normalized to represent relative slope gradient. | Unitless (normalized; higher values = steeper slopes) | No missing values. | | Aspect | N/A | Orientation of the slope (e.g., north, south), normalized to a relative scale. Reflects solar radiation exposure and moisture retention potential. | Unitless (normalized) | No missing values. | | Dis\_fault | Dis\_fault (abbreviation for "Distance to Fault") | Proximity to geological faults, normalized to a relative scale. | Unitless (normalized; lower values = closer to faults) | No missing values. | | Dis\_road | Dis\_road (abbreviation for "Distance to Road") | Proximity to roadways, normalized to a relative scale. | Unitless (normalized; lower values = closer to roads) | No missing values. | | NDVI | NDVI (abbreviation for "Normalized Difference Vegetation Index") | Metric of vegetation cover density, derived from remote sensing. | Unitless (normalized; higher values = denser vegetation) | No missing values. | | Lithology | N/A | Normalized index representing geological composition (e.g., rock type, soil texture) of the sampling site. | Unitless (normalized) | No missing values. | | SPI | SPI (abbreviation for potential "Soil Protection Index" or "Stream Power Index") | Normalized index capturing soil erodibility or hydrological energy, reflecting potential for surface disturbance. | Unitless (normalized) | No missing values. | #### Missing Values No missing values are present in this dataset. All cells contain valid numerical entries. In extended versions of this dataset, missing values would be explicitly indicated by the string "NA" to ensure clarity and consistency in data processing. ## Code/software ### Code/Software The dataset (in CSV format) can be accessed and processed using a range of free, open-source, or widely available software tools. Below is a detailed description of the software, versions, packages, and workflows used for data handling and visualization, as specified (Python for programming and ArcGIS for visualization): #### 1. Python (Open-Source Programming Language) Python is a free, open-source programming language used for data loading, preprocessing, and preliminary analysis of the landslide susceptibility dataset. It is ideal for handling tabular data (e.g., CSV files) and supports reproducible data workflows. * **Software Version**: Python 3.8 or higher (compatible with 3.9–3.11). * **Required Packages**: * `pandas` (version 1.3.0+): Used to read, manipulate, and clean the CSV dataset (e.g., loading data with `pd.read_csv()`, checking for missing values, and computing descriptive statistics). * `numpy` (version 1.21.0+): Supported numerical operations for analyzing continuous variables (e.g., calculating mean, variance of slope, elevation, or NDVI values). * `matplotlib` (version 3.4.0+) / `seaborn` (version 0.11.0+): Optional for preliminary non-spatial visualizations (e.g., histograms of slope steepness or scatter plots of NDVI vs. lithology) to explore relationships between variables. * **Workflow in Python**: 1. Load the CSV file using `pandas.read_csv("pingjiang_landslide_susceptibility_samples.csv")` to create a DataFrame. 2. Validate data integrity (e.g., check for duplicates via `df.duplicated()` or missing values via `df.isnull().sum()`—none were found in this dataset). 3. Perform exploratory data analysis (EDA), such as generating summary statistics (`df.describe()`) or visualizing distributions of key variables (e.g., `sns.histplot(data=df, x="Slope")` to assess slope steepness ranges). #### 2. ArcGIS (Geospatial Visualization Software) ArcGIS (ESRI) was used for spatial visualization of the dataset, leveraging its capabilities to map and analyze geographic patterns of landslide-related variables. While ArcGIS is not fully open-source, it is widely accessible via academic licenses or free trial versions (e.g., ArcGIS Pro trial). * **Software Version**: ArcGIS Pro 2.8 or higher (compatible with 3.0+). * **Key Functionality**: * Importing the CSV dataset as a table and linking it to spatial coordinates (assumed to be preprocessed or associated with geographic points in the study area). * Creating thematic maps to visualize spatial distributions of variables (e.g., gradient maps of slope steepness, NDVI-derived vegetation cover, or proximity to faults/roads). * Overlaying variables to explore spatial correlations (e.g., areas with high slope values coinciding with low NDVI, indicating potential landslide susceptibility). * **Workflow in ArcGIS**: 1. Import the CSV file into ArcGIS Pro via "Add Data" and convert it to a point feature class (using associated geographic coordinates, not explicitly listed in the dataset but assumed for spatial reference). 2. Use "Symbology" tools to classify and visualize each variable (e.g., quantile classification for "Elevation" to highlight high vs. low elevation zones). 3. Generate composite maps by overlaying multiple variables (e.g., slope + lithology) to identify spatial clusters of factors linked to landslide risk. ## Access information Other publicly accessible locations of the data: * https://zrzyt.hunan.gov.cn/ Data was derived from the following sources: *