Shasha Tian

1. Research Questions & Background

Objectives: Identify national-scale spatiotemporal distribution laws of wildfire occurrence in China and quantify the relative impact of natural environmental vs. socioeconomic drivers.

2. Data & Methodology

Data: GFEDv4 wildfire satellite database (2003–2016), meteorological datasets (temperature, precipitation), terrain slope data, and socioeconomic indicators (population density, GDP).
Methodology: ArcGIS spatial tools (Kernel Density Analysis, Standard Deviational Ellipse, Center-of-Gravity Migration Analysis, Linear Regression Trend Analysis) and Generalized Additive Models (GAM) to capture non-linear relationships.

3. Key Results & Personal Contribution

Findings: Revealed clear seasonal and regional clustering of wildfire occurrences. GAM modeling demonstrated that climate variables (temperature and precipitation) and terrain slope exert non-linear influences on wildfire frequency, moderated by human activity intensity (population and GDP).