Population dispersal is an important life history trait that is influenced by environmental change, and it can alter the distribution, structure, and abundance of a population. In addition, population dispersal allows a species to actively adapt and ensure long-term survival. Patterns of population dispersal can provide key information about the rules and mechanisms of how populations disperse, and they are an important basis for conservation management. Methods for studying population dispersal in large animals are lacking, which therefore restricts the development and application of dispersal ecology. Two crucial issues that need to be taken into account when considering dispersal patterns are population distribution and abundance. Based on the factors of dispersal pattern and the giant panda characteristics of population and home range, this study intends to (1) reveal the dispersal patterns of giant pandas in the Qinling Mountains by comparing the change in their population distribution and aggregation from 2000 to 2012, and (2) explore methods for studying large animal population dispersal. Based on signs of giant pandas obtained from the third and fourth national surveys conducted by the Chinese Forestry Administration (completed in 2000 and 2012, respectively), a circular extension region with a giant panda sign as the center was produced using the buffer function in ArcGIS10.0. The average diameter of the home range of giant pandas was defined as 3 km. Subsequently, using the dissolve function in ArcGIS, we created polygons based on these circles, and established the primary population distribution area around the outer boundary of the polygons. We identified the population dispersal area based on the change in distribution range. Additionally, we mapped population aggregation densities in 2000 and 2012, and divided the population distribution range into areas with different aggregation densities by employing the kernel density analysis function of ArcGIS. We also revealed the population abundance and direction of population dispersal based on the variation in population aggregation. We found that the population distribution area of giant pandas increased by 15307.8 hm² in the Qinling Mountains since 2000, with an obvious expanding trend in the northwestern and southwestern regions of the study area. However, the population distribution decreased in the eastern and southern regions. Furthermore, the degree of population aggregation increased, especially for areas with medium aggregation density, and two patches of high-density aggregation became one. In addition, the integrity of the population aggregation pattern also greatly improved. In this way, the population pattern showed a detectable trend of expansion and an increase in population aggregation. This study revealed the area and direction of giant pandas dispersal in the Qinling Mountains since 2000, which has important implications for current population safety and conservation. Our study also showed that the population dispersal patterns of giant pandas could be effectively determined with the spatial variation in population distribution and abundance that is based on biological characteristics and long-term monitoring. The methodology developed in this study, combined with ongoing monitoring programs in Chinese nature reserves, can facilitate the study of population dispersal in large animals.